Submitted on January 10th, approved on January 12th — taking just over 2 days.
If not submitted on a weekend (January 10th was Saturday), the review time might have been even shorter. Compared to the previous MoneyMind App’s first launch which experienced 14 days, 4 submissions, and 3 rejections, HeartPetBond’s review process was smooth sailing.
Since I had successfully launched MoneyMind App before, I had encountered many pitfalls. This time, I proactively avoided these issues:
Screen Compatibility: MoneyMind was rejected for text overlap issues. This time, AI-autonomous testing prioritized verification across different screen sizes
Account Deletion: App Store requires account deletion functionality. This time, it was built-in from the start
Feature Completeness: Ensured all promoted features work properly, with no “coming soon” placeholders
Compared to using Opus 4.5 for MoneyMind development, GLM 4.7 costs only about 1/7, but under the BMAD agile process, it’s fully capable of handling App development tasks.
Core Conclusion: AI autonomous software app development has been validated — 1 week for PoC, 2 weeks to submit to App Store. Humans have no advantage in software development execution.
This week’s biggest milestone is successfully validating that Vibe Coding AI-Inspired Programming has achieved a complete closed loop of low-cost, rapid software app development.
Starting January 4, using Claude Code Skills + GLM 4.7 workflow to automatically collect and write 50+ WeChat articles, now published to WeChat draft box. Each article is manually reviewed before publishing.
Manual review of each article takes a few minutes, but with large article counts, it becomes tedious. This is the only remaining manual step and the efficiency bottleneck.
A popular article on X about a foreign female executive using Claude Code for AI autonomous work and life planning.
Reading through it, the main capability is Claude Code’s engineering and ecosystem. I can do this too — and much of my current daily work like rapid app development, batch WeChat article publishing — has already been automated via Claude Code.
Technically no difficulty at all. Risks are mainly in user sensitive data acquisition and usage. If developing, need more consideration on product design and legal compliance.
Software is just an idea. We can propose ideas and let AI autonomously help implement them.
Compared to the internet filled with anxiety, noise, and meaningless arguments, we should calm down and think about the future of software development.
TL;DR: Using the Chinese LLM GLM 4.7 with Claude Code + BMAD V6 + Flutter MCP, fully autonomous AI development from idea to App Store submission in just 12 days. The project contains 62 Dart files with 15,190 lines of code.
Part 1: The Beginning — A British Shorthair Blue Cat
Recently, my family and I adopted a British Shorthair blue cat. Every day is filled with feeding, watering, playing, and cleaning the litter box — we’re having a blast!
While caring for this fluffy little companion, an idea came to me: What if there was an app to record family members’ interactions with the cat — feeding, exercise, playtime, and more? This would not only help track the cat’s health but also let the whole family participate in pet care together, strengthening our emotional bonds.
Part 2: Technology Stack — Standing on the Shoulders of Giants
In the Vibe Coding (AI autonomous development) space, the technology has become quite mature. Previously, I used Claude Code + Opus 4.5 + BMAD V6 + Flutter MCP to let AI autonomously develop a MoneyMind App. The entire development process took only 8 hours of interaction with AI, and it has been successfully approved and published on the App Store.
Recently, Chinese AI company Zhipu released their latest GLM 4.7 large language model:
Context Window: 200K Tokens (same as Claude Code’s model)
Maximum Output: 64K Tokens
Price Advantage: Much cheaper than Opus 4.5
Performance: According to community reviews, slightly behind Opus 4.5 but with excellent cost-effectiveness
Since Vibe Coding for autonomous app development is now mature, cost-effective, fast, and requires almost no human involvement — with AI generating UI and completing automated test verification throughout — I decided to run another experiment: Can Zhipu’s GLM 4.7 model (from the recently Hong Kong-listed company) also quickly develop an app and submit it to the App Store?
Selected Quick Flow Dev mode — BMAD’s rapid development flow that doesn’t require generating and maintaining verbose documentation, letting AI focus on code generation and testing.
This experiment largely achieved its goals: Even with the budget-friendly GLM 4.7 model, AI can autonomously develop a complete app. I only provided the initial idea, and the rest — design, coding, testing — was all handled by AI with minimal human involvement.
The era of AI autonomous app development has arrived. With a good idea and mature AI toolchains (Claude Code + BMAD + LLMs), anyone can quickly turn ideas into reality.
Life is compounding. Build your pet bond with AI today!
The journey of MoneyMind (省钱思维) from concept to App Store approval—a testament to AI-autonomous software development powered by Claude Code, BMAD V6, and Flutter MCP.
This morning, I logged into App Store Connect and noticed something special: the App status had changed from the yellow “Waiting for Review” dot to a green “Ready for Submit” indicator.
A moment of excitement—Claude Code + BMAD V6 + Flutter MCP had enabled AI to autonomously develop the MoneyMind savings mindset app, which had now passed Apple’s official review and was ready for release.
The journey began in early November, with sporadic development continuing through early December. Finally, just before Christmas Eve, the app was submitted for App Store review.
Holiday delays - Christmas, year-end, and New Year holidays extended the typical review cycle
Screen compatibility issues - Fully AI-autonomous testing missed some screen size compatibility issues, resulting in text overlap and missing features discovered during review
With approval granted, I wanted to celebrate—but first, a real test. I downloaded and installed the app, started a savings plan mode, and set a goal: save an extra 1,000 yuan for investment by January 31st.
Enable AI to autonomously develop software—including generating code and test code, automatically running UI automated integration tests to verify app functionality. Humans only provide the initial idea, then leave everything to AI with minimal or no human intervention throughout the process.
Breadth: AI autonomously completes development, testing, verification, and deployment for apps, WeChat mini-programs, API services, web platforms, and games.
Depth: Beyond Claude Code, Opus, and BMAD v6—explore alternative tools like GLM 4.7 and other software development processes for AI-autonomous completion.
Intensity: Further reduce human involvement—begin “AI desensitization,” maintain appropriate distance from AI systems.
Concurrency: Enable AI to simultaneously, asynchronously, and autonomously advance multiple software projects.
Vibe Coding AI-driven software development is technically mature and reliable. The market prospects are vast—this is not a zero-sum game. Both professional software engineers and everyday users can benefit.
MoneyMind’s journey from concept to App Store approval demonstrates that AI-autonomous software development is not just possible—it’s here. With the right combination of tools (Claude Code + BMAD V6 + Flutter MCP) and a clear vision, AI can handle the complete software development lifecycle with minimal human intervention.
The future of software development is autonomous, collaborative, and accessible to everyone.
From zero to agentic coding in 30 minutes. Transform a fresh cloud server into a fully-configured agentic coding environment with Claude Code, OpenAI Codex, and Google Gemini—all pre-configured with 30+ modern developer tools. All totally free and open-source.
The rise of AI coding agents like Claude Code, Codex CLI, and Gemini CLI has created a new paradigm in software development. These agents can write production code, debug issues, and architect solutions—but only if they have the right environment.
The barrier isn’t the agents themselves. It’s the hours of setup required to create an environment where agents can be productive:
AI agents consume significant RAM and CPU. Running them locally drains your battery and slows everything down. Each agent uses ~2GB RAM—with 10+ agents, you need 48-64GB, more than most laptops have.
Cloud giants charge by the hour with unpredictable billing. A dedicated VPS is simpler and cheaper. A 64GB VPS costs ~$40-56/month flat—equivalent cloud resources would cost 3-5x more.
Your VPS runs 24/7. Queue up tasks before bed, wake up to completed code. AI agents can refactor, test, and iterate autonomously—compounding progress overnight.
Vibe mode is designed for throwaway VPS environments where velocity matters more than safety:
Passwordless sudo eliminates friction
Agent dangerous flags skip confirmation dialogs
Pre-configured aliases for maximum speed
Warning: Never use vibe mode on production or shared systems. It’s intentionally insecure for velocity—use only on ephemeral VPS instances you can rebuild if needed.
Agent Flywheel isn’t just a collection of tools—it’s a carefully curated system where each component amplifies the others. The value is multiplicative, not additive.
Better Environment → More Agent Productivity → More Code Written
ACFS provides reproducible, idempotent setup—ensuring every team member’s VPS environment is identical, eliminating “works on my machine” for agentic workflows.
I built Agent Flywheel because I was being inundated with requests from friends, relatives, and strangers asking me to help them get started with using AI for software development.
I wanted one resource I could point people to that would help them “from soup to nuts” in getting set up—even if they have almost no computer expertise, just motivation and desire.
This is also a platform to share my suite of totally free, open-source agentic coding tools—originally built for my consulting work with Private Equity and Hedge Funds, now available to help others be more productive and creative too.
In the rapidly evolving world of AI-assisted development, efficient tooling is paramount. CCometixLine emerges as a powerful solution for developers using Claude Code, offering a high-performance statusline written in Rust that integrates seamlessly with your workflow.
CCometixLine is a statusline tool designed specifically for Claude Code. Built with Rust, it prioritizes performance and reliability while delivering a rich set of features including Git integration, usage tracking, and an interactive Terminal User Interface (TUI) for configuration.
Unlike standard statuslines, CCometixLine is built with Rust, ensuring minimal latency even with complex git operations and transcript analysis. Its rich feature set, combined with the ease of configuration via TUI, makes it an essential tool for power users looking to optimize their daily workflow with Claude Code.
CCometixLine represents a significant upgrade to the default Claude Code experience. Whether you need better visibility into your git status, want to track your token usage more effectively, or simply desire a customized aesthetic, CCometixLine delivers on all fronts.
Give it a try and elevate your coding setup today!
Claude Code has revolutionized how developers interact with AI, but many users are intimidated by the concept of “Skills” — custom capabilities that extend Claude’s functionality. AI Will (@FinanceYF5), a growth consultant and AI industry analyst, has created a comprehensive tutorial that breaks down this barrier, enabling non-technical users to set up Claude Skills in just 15 minutes.
This blog post explores AI Will’s approach and provides additional context to help you get started with Claude Skills quickly and confidently.
Claude Skills are reusable, custom capabilities that you can create to extend Claude Code’s functionality. Think of them as personal tools that automate repetitive tasks, encode your preferences, or integrate with external services. Skills can range from simple text formatting utilities to complex multi-step workflows.
According to research from METR (mentioned in Reza Rezvani’s analysis), developers using AI tools often complete tasks slower than those without AI, while paradoxically rating their productivity higher. This productivity gap exists because users focus on generating more code rather than building reusable capabilities that compound over time.
Skills are the solution to this problem. They transform one-off prompts into persistent, refinable tools that actually accelerate your workflow.
AI Will’s tutorial, shared across multiple X (Twitter) threads, emphasizes accessibility above all else. The core philosophy is that Claude Skills should not be intimidating — despite appearances, they are remarkably simple to set up.
Activate your skill by referencing it in conversation: “Use my code-style skill.” Claude will apply those preferences automatically. Iterate based on results.
Claude Skills are the key to unlocking Claude Code’s full potential. AI Will’s 15-minute tutorial removes the intimidation factor and provides a clear path for users of all technical backgrounds. By investing just 15 minutes, you can begin building a library of reusable capabilities that will pay dividends in every future session.
The barrier to entry is low — the only limit is your imagination.
In an effort to accelerate content creation, an experiment was conducted to write multiple blog posts simultaneously using Claude Code. However, this aggressive parallelization strategy hit a bottleneck: the GLM API quota limits. This unexpected constraint led to content loss, with several blog posts failing to complete.
To maximize efficiency, over 10 instances of Claude Code shells were launched in parallel. The goal was to generate a batch of blog posts concurrently.
As the multiple instances began generating content, they rapidly consumed the available API quota. The GLM API, having strict rate limits and quotas, blocked further requests once the threshold was reached.
The system immediately flagged the quota violation.
The direct result of hitting the API limit was that the generation process for several blog posts was abruptly terminated. This left the files in an incomplete state, effectively resulting in content loss.
Checking the WeChat Official Account Draft Box confirmed that the articles were not successfully fully generated or uploaded.
While parallel processing is a powerful technique for efficiency, it must be balanced against external constraints like API quotas. Future batch processing will need to implement rate limiting or sequential queuing to ensure stability and data integrity.
Sub-Agents Directory is a carefully curated platform hosting a collection of Claude Code sub-agents and MCP servers. For developers using Claude Code, it’s an invaluable resource for quickly finding specialized AI assistant prompts tailored to specific technology stacks, frameworks, or development scenarios.
Summary: Sub-Agents Directory is an essential resource for Claude Code users. Whether you’re looking for specific technical expertise or learning how to write high-quality prompts, this platform delivers tremendous value. Visit sub-agents.directory today to start your specialized AI programming journey!
What if you could learn a powerful AI development tool by having the AI itself teach you? That’s exactly what Carl Vellotti’s Claude Code for Product Managers course delivers—a completely free, interactive course that runs inside Claude Code.
This isn’t another video course or documentation site. It’s an immersive learning experience where Claude guides you through each module interactively, and you learn by doing real PM work with real files.
This course and its creator are not affiliated with, endorsed by, or sponsored by Anthropic. Carl is an independent creator who loves Claude Code and wanted to help other Product Managers learn how to use this tool effectively.
Claude Code represents a fundamental shift in how Product Managers can work with AI—not as a chatbot bolted onto workflows, but as an integrated partner that works directly with files, data, and documents.
Carl Vellotti’s course is the best resource I’ve found for PMs to learn this tool. The fact that it’s:
100% free
Interactive (learn inside the tool)
Practical (real PM scenarios)
Comprehensive (from basics to advanced workflows)
…makes it an invaluable resource for any Product Manager looking to level up their AI-assisted productivity in 2026.
You’ve been there. You open Claude Code, ready to build something amazing. You see everyone else shipping apps, workflows, full products. And then… you stare at a blank terminal for 20 minutes and close it.
The problem isn’t that you lack creativity. The problem is you’re too close to your own work.
You stopped noticing the friction.
The Solution: Let Claude Find Opportunities for You
The fix isn’t finding a better project idea. It’s letting the tool find it for you.
Claude Code is better at identifying opportunities than you are. Why? Because you’re too deep in your daily workflow to see the patterns. The repetitive tasks. The manual processes. The things you’ve been meaning to fix for months.
In the rapidly evolving landscape of AI coding tools, developers often face a fragmentation problem: premium subscriptions are locked behind OAuth authentication, while many tools and SDKs expect standard API keys. CLIProxyAPI elegantly solves this bridge by converting OAuth-based AI services into universal API endpoints.
CLIProxyAPI is an open-source proxy server written in Go that wraps multiple AI CLI tools and exposes them through standardized API interfaces. It transforms your existing AI subscriptions into OpenAI/Gemini/Claude-compatible APIs, eliminating the need for separate API keys.
CLIProxyAPI represents a pragmatic solution to AI service fragmentation, transforming OAuth-based subscriptions into universal API endpoints. Whether you’re integrating with coding tools, balancing load across accounts, or simply avoiding API key management, CLIProxyAPI provides a robust, open-source foundation.
With active development (1,239+ commits), comprehensive documentation in both English and Chinese, and a growing ecosystem of companion tools, CLIProxyAPI is poised to become an essential utility in the AI developer’s toolkit.
If you’re a developer who uses CLI-based AI tools like Claude Code, Codex, or Gemini CLI, you know the pain of managing hundreds of scattered session files. Finding that brilliant solution from last Tuesday, resuming a suspended project context, or even just reviewing what you accomplished can be frustrating.
Enter CodMate—a native macOS SwiftUI application that brings order to your AI coding chaos. Built with performance in mind and designed for developers who value their time, CodMate transforms how you interact with your AI programming assistants.
CodMate is a session manager for CLI AI tools that provides:
Multi-source session browsing supporting Codex, Claude Code, and Gemini CLI
Blazing-fast search with incremental indexing and caching
Compact three-column UI designed for efficient workflow
“Ship it” workflows like Project Review with Git Changes
One-click Resume/New functionality
Built with Swift 6 and SwiftUI, CodMate leverages macOS 13.5+ APIs to deliver a truly native experience with universal binary support (Apple Silicon + Intel).
A: No, CodMate only reads session files. Notes, project assignments, and other metadata are stored separately.
Q: Can I use CodMate with multiple CLI tools simultaneously?
A: Yes! CodMate supports Codex, Claude Code, and Gemini CLI concurrently.
Q: Is my session data sent anywhere?
A: CodMate is local-first. Your session data never leaves your Mac unless you explicitly use features that call external APIs (like AI commit message generation).
Q: How does CodMate handle large session files?
A: CodMate uses intelligent parsing—reading only what’s needed for display, with background enrichment for full content. This keeps the UI fast even with thousands of sessions.
CodMate fills a crucial gap in the AI programming workflow by providing a professional-grade interface for managing the hundreds or thousands of conversations we have with our AI assistants. If you live in the terminal with Claude Code, Codex, or Gemini CLI, CodMate is the tool that brings order to the chaos—letting you focus on building rather than hunting for that one perfect response from last week.
Agent Client is an Obsidian plugin that brings AI coding agents directly into your knowledge management workspace. Built on the Agent Client Protocol (ACP)—an open standard pioneered by the Zed editor—this plugin creates a seamless bridge between your personal knowledge base and powerful AI coding assistants like Claude Code, Codex, and Gemini CLI.
Instead of juggling between your notes and terminal, Agent Client integrates AI agents into a dedicated right-side panel within Obsidian, transforming your vault into an intelligent development companion.
Reference your Obsidian notes directly in conversations using @notename syntax. Automatically include the active note’s content or manually reference specific notes to provide context to your AI agent.
Switch between Claude Code, Codex, Gemini CLI, and any ACP-compatible custom agents. Configure multiple agents and switch between them based on your task requirements.
Let your agent execute shell commands and return results inline in the chat. Run build scripts, git commands, or any terminal operation without leaving the conversation.
Code Review with Context: Open your project documentation in Obsidian, reference it with @notename, and ask Claude Code to review your implementation against the documented requirements.
Debugging Session: When stuck on a bug, take a screenshot, paste it into the Agent Client chat, and let your AI agent analyze the error while you reference relevant error logs stored in your vault.
Documentation Generation: As you build features, use the chat to generate documentation while the agent reads your code files—then save the output directly to your notes.
Multi-Agent Task Switching: Use Claude Code for architectural planning, switch to Codex for code generation, and use Gemini CLI for alternative perspectives—all within the same interface.
Q: Can I use multiple agents simultaneously?
A: Currently, you can switch between agents, but future updates will include multi-instance support for running multiple agents simultaneously.
Q: Does this work on mobile?
A: The plugin is designed for desktop Obsidian. Mobile support depends on Obsidian’s plugin capabilities on mobile platforms.
Q: Can I use custom agents?
A: Yes! Any agent that implements the Agent Client Protocol (ACP) can be configured in the plugin settings.
Unique Advantage: Agent Client’s tight integration with Obsidian’s knowledge management makes it ideal for developers who maintain extensive documentation, research notes, or project specifications in their vault.
Agent Client transforms Obsidian from a passive knowledge repository into an active development companion—where your notes, code, and AI agents work together seamlessly.
Throughout December, I shared one Claude Code tip per day on X/Twitter and LinkedIn. What started as a simple advent calendar became a map of the features that have fundamentally changed how I write software. This post compiles all 31 tips into a comprehensive guide, reorganized from beginner essentials to advanced patterns, and adds additional context that can’t be covered in 280 characters.
Whether you’re just getting started or looking to level up with Claude Code, there’s something here for you.
Everybody needs onboarding docs. With /init, Claude writes its own.
Claude reads your codebase and generates a CLAUDE.md file with:
Build and test commands
Key directories and their purposes
Code conventions and patterns
Important architectural decisions
This is the first thing I run in any new project.
For larger projects, you can also create a .claude/rules/ directory for modular, topic-specific instructions. Each .md file in this directory is automatically loaded as project memory alongside your CLAUDE.md. You can even use YAML frontmatter to apply rules conditionally based on file paths:
---
paths: src/api/**/*.ts
---
# API Development Rules
- All API endpoints must include input validation
Think of CLAUDE.md as your general project guide, and .claude/rules/ as focused supplements for testing, security, API design, or anything else that deserves its own file.
Want to save something to Claude’s memory without manually editing CLAUDE.md? As of Claude Code 2.0.70, just tell Claude to update the Claude.md file for you.
Just tell Claude to remember it:
“Update Claude.md: always use bun instead of npm in this project”
Don’t waste tokens asking “can you run git status?”
Just type ! followed by your bash command:
Terminal window
!gitstatus
!npmtest
!ls-lasrc/
The ! prefix executes bash instantly and injects the output into context. No model processing. No delay. No wasted tokens. No need for multiple terminal windows.
This seems small until you realize you’re using it fifty times a day.
Accidentally closed your terminal? Laptop died mid-task? No problem.
Terminal window
claude--continue# Picks up your last conversation instantly
claude--resume# Shows a picker to choose any past session
Context preserved. Momentum restored. Your work is never lost. You can also customize how long sessions are preserved via the cleanupPeriodDays setting. By default it is 30 days, but you can set it as long as you want, or even 0 if you don’t want to preserve your Claude Code sessions.
Start a task on the web, finish it in your terminal:
Terminal window
# On claude.ai/code, start a Claude Code session
# It runs in the background while you're away
# Later, from your terminal:
claude--teleportsession_abc123
This pulls and resumes the session locally. Claude at home and on the go. Also works via the Claude mobile app for iOS and Android, as well as the Claude Desktop app.
Tired of reaching for the mouse to edit your prompts?
Type /vim and unlock full vim-style editing:
Command
Action
h j k l
Navigate
ciw
Change word
dd
Delete line
w b
Jump by word
A
Append at end of line
Edit prompts at the speed of thought. Your muscle memory from decades of vim use finally pays off in an AI tool. And it’s never been easier to exit vim with Claude Code, just type /vim again.
Trigger extended thinking on demand with a single keyword:
> ultrathink: design a caching layer for our API
When you include ultrathink in your prompt, Claude allocates up to 32k tokens for internal reasoning before responding. For complex architectural decisions or tricky debugging sessions, this can be the difference between a surface-level answer and genuine insight.
In the past you were able to specify think, think harder, and ultrathink to allocate different amounts of tokens for thinking, but we’ve since simplified this into a single thinking budget. The ultrathink keyword only works when MAX_THINKING_TOKENS is not set. When MAX_THINKING_TOKENS is configured, it takes priority and controls the thinking budget for all requests.
Configure them via /hooks or in .claude/settings.json.
Use hooks to block dangerous commands, send notifications, log actions, or integrate with external systems. It’s deterministic control over probabilistic AI.
Skills are folders of instructions, scripts, and resources that teach Claude specialized tasks.
They’re packaged once and usable everywhere. And since Agent Skills are now an open standard, they work across any tool that supports them.
Think of skills as giving Claude expertise on demand. Whether that’s your company’s specific deployment process, a testing methodology, or a documentation standard.
Remember when sharing your Claude Code setup meant sending 47 files across 12 directories?
That era is over.
Terminal window
/plugininstallmy-setup
Plugins bundle commands, agents, skills, hooks, and MCP servers into one package. Discover new workflows via the marketplace, which includes search filtering for easier discovery.
The same agent loop, tools, and context management that power Claude Code are now available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code:
When I started this advent calendar, I thought I was just sharing tips. But looking back at these 31 days, I see something more: a philosophy of human-AI collaboration.
The best features in Claude Code are about giving you control. Plan mode. Agent Skills. Hooks. Sandbox boundaries. Session management. These are tools for working with AI, not surrendering to it.
The developers who get the most out of Claude Code aren’t the ones who type “do everything for me.” They’re the ones who’ve learned when to use Plan mode, how to structure their prompts, when to invoke ultrathink, and how to set up hooks that catch mistakes before they happen.
AI is a lever. These features help you find the right grip.
Here’s to 2026.
Originally published by Ado Kukic as “Advent of Claude: 31 Days of Claude Code” on adocomplete.com.
“I’m Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit.”
— Boris Cherny, January 2, 2026
When the creator of Claude Code shares his personal workflow, the development community pays attention. Boris Cherny recently revealed his surprisingly “vanilla” yet highly sophisticated approach to maximizing AI-assisted development productivity.
At the core of Boris’s workflow are two fundamental mindsets:
Compound Thinking - The CLAUDE.md file isn’t a one-time document but a continuously evolving knowledge base accumulated through daily work, code reviews, and problem discovery
Verification Loops - Verification can improve output quality by 2-3x, addressing a core limitation in current AI programming: AI generates code well, but doesn’t inherently know if it actually works or provides good UX
Boris emphasizes: “A good plan is half the success” and invests heavily in upfront planning before switching to auto-accept mode for implementation.
Multi-Instance Parallelism: 15-20 Claude Instances Running Simultaneously
Boris uses only Opus 4.5 with thinking mode enabled. His rationale is counterintuitive but proven:
“Although Opus is larger and slower, because of its strong understanding and accurate tool usage, it’s actually faster overall. No need for repeated corrections and guidance—it gets it right the first time. That’s true efficiency.”
The upfront cost of larger models pays dividends through reduced iteration cycles and higher-quality first-pass outputs.
During code reviews, Boris uses @.claude tags on colleagues’ PRs, instructing Claude to add discovered issues to CLAUDE.md. This is powered by Claude Code’s GitHub Action (installed via /install-github-action).
This is their version of “compound engineering”—every code review systematically improves the knowledge base, which then automatically improves future code quality.
Most sessions start with Plan mode (activated via double Shift+Tab). Boris discusses back and forth with Claude until satisfied with the plan, then switches to auto-accept edit mode where Claude typically completes implementation in one pass.
The pattern reinforces the principle: invest in planning, execute with confidence.
The team uses a Post-Tool-Use hook to auto-format Claude-generated code. Since Claude typically generates well-formatted code, the hook only handles the final ~10%, preventing CI format failures while maintaining code style consistency.
Permissions Management: Pre-Allow Rather Than Skip
Boris doesn’t use --dangerously-skip-permissions. Instead, he uses /permissions to pre-allow common, safe bash commands in his environment, avoiding unnecessary permission prompts while maintaining security awareness.
Configuration is stored in .claude/settings.json and shared with the team via Git.
“The most important thing is giving Claude a way to verify its work. With this feedback loop, final result quality improves 2-3x.”
The Claude Code team uses the Claude Chrome extension to test every change. Claude opens the browser, tests the UI, and iterates until code works and user experience is satisfactory.
Verification methods vary by domain—running a bash command, executing test suites, or testing in browser/mobile emulators—but investing in robust verification is universally applicable.
Opus 4.5 exclusivity - Larger models are faster overall
Parallel execution - 15-20 instances working simultaneously
Shared knowledge bases - Team learning through Git-synced CLAUDE.md
The setup might be “surprisingly vanilla” in configuration, but the methodology represents a sophisticated rethinking of AI-assisted development—transforming from single-shot interactions to a continuously learning, parallel-processing, verification-driven development ecosystem.
“The granularity of productivity has been completely transformed. In the past, we were discussing how to write a good function, while Boris is discussing how to schedule an intelligence matrix composed of 20 Opus 4.5s.”
In January 2026, Anthropic released Claude Code in Action — a comprehensive official course designed to teach developers how to harness the full power of Claude Code, their command-line AI assistant. Available on both Anthropic Skilljar and Coursera, this free course bridges the gap between basic AI assistance and professional-grade AI-powered development workflows.
“This course covers Claude Code, a command-line AI assistant that uses language models to perform development tasks.” — Anthropic
The course emphasizes that effective context management is the single most important skill for using Claude Code productively. The /init command and CLAUDE.md files aren’t conveniences — they’re essential tools.
The MCP ecosystem transforms Claude Code from a capable assistant into an extensible platform. The course provides just a taste of what’s possible with community servers.
Claude Code in Action is a well-structured, practical introduction to Anthropic’s AI coding assistant. It won’t make you an expert overnight, but it provides the foundation needed to use Claude Code effectively in professional development workflows.
Value Rating: ⭐⭐⭐⭐☆ (4/5)
Beginners: Essential starting point
Intermediate Users: Good for filling knowledge gaps
Experts: May find it basic
The course is free, takes about an hour, and delivers tangible value. If you’re using Claude Code but feel like you’re missing features, this course will likely unlock capabilities you didn’t know existed.
“The best way to learn Claude Code is to use Claude Code to learn Claude Code.” — Course Philosophy
TL;DR: Claude HUD by jarrodwatts is a Claude Code plugin that displays real-time session information — context usage, tool activity, agent status, and todo progress — right below your input. No separate window, no tmux required, updates every ~300ms.
Have you ever found yourself mid-conversation with Claude Code, wondering:
How much context do I have left?
Which subagent is stuck on what task?
What tools has Claude been using?
Claude HUD answers all these questions at a glance. It’s a heads-up display that shows exactly what’s happening in your Claude Code session, always visible below your input area.
Claude HUD is a plugin that extends Claude Code’s statusline API to provide real-time session visibility. It works by:
Reading Claude Code’s native token data (not estimates)
Parsing the transcript for tool/agent activity
Updating the display every ~300ms
Key Insight: Unlike external monitoring tools, Claude HUD uses Claude Code’s built-in statusline API — meaning it works in any terminal, requires no separate window or tmux setup, and displays accurate data directly from the source.
Claude HUD is a small plugin with a big impact: it makes the invisible visible. By showing context usage, tool activity, agent status, and todo progress in real-time, it gives you the situational awareness needed to use Claude Code more effectively.
Whether you’re a heavy user of agents, working with large codebases, or just want better visibility into your sessions, Claude HUD is worth installing.
TL;DR: Claudian by YishenTu embeds Claude Code as a sidebar chat interface in Obsidian, giving your vault full agentic capabilities — file read/write, bash commands, vision support, MCP servers, and multi-step workflows — all while seamlessly integrating with your notes.
If you’re like me, your Obsidian vault is more than just a note-taking app — it’s your second brain, your project hub, your knowledge base. Now imagine having Claude Code’s full AI agent capabilities integrated directly into that workspace.
That’s exactly what Claudian delivers. It’s an Obsidian plugin that embeds Claude Code as a sidebar chat interface, turning your vault into Claude’s working directory with complete agentic capabilities.
Why this matters: Unlike traditional chatbots or simple AI assistants, Claudian gives Claude actual agency — it can read, write, and edit files in your vault, execute bash commands, use tools, run multi-step workflows, and even connect to external services via MCP. All while staying contextually aware of your notes.
Claudian is an Obsidian plugin built on the Claude Agent SDK that brings Claude Code’s capabilities directly into your Obsidian workspace. Think of it as:
A Sidebar Chat: Click the bot icon or use the command palette to open Claude in a sidebar panel
Full Agentic Power: Claude can read/write files, run bash commands, and execute multi-step workflows within your vault
Context-Aware: Automatically attaches your focused note, lets you @mention files, and includes selected text
Vision Support: Analyze images via drag-drop, paste, or file path
Extensible: Supports slash commands, custom instructions, skills, and MCP servers
Key Insight: This isn’t just a chat interface — it’s Claude Code running inside Obsidian with your vault as its working directory.
Claudian represents a significant step forward in AI-assisted knowledge work. By bringing Claude Code’s full agentic capabilities into Obsidian, it transforms your vault from a static note repository into an interactive workspace where Claude can read, write, analyze, and collaborate — all while maintaining deep context awareness of your work.
Whether you’re writing documentation, managing projects, or building a knowledge base, Claudian offers a powerful new way to work with AI that feels natural, integrated, and genuinely useful.
In the rapidly evolving landscape of AI-powered development tools, OpenCode has emerged as a formidable open-source alternative to commercial AI coding assistants. But if you’re a hacker who loves customization and maximum control, Oh My OpenCode is the plugin that transforms OpenCode into something extraordinary.
Created by developer code-yeongyu after spending $24,000 worth of tokens on personal development with various AI tools, Oh My OpenCode represents the distillation of countless hours of testing, configuration, and real-world usage. It’s described by its author as “OpenCode on steroids” — and for good reason.
“If Claude Code does in 7 days what a human does in 3 months, Sisyphus does it in 1 hour. It just works until the task is done. It is a disciplined agent.” — B, Quant Researcher
At the heart of Oh My OpenCode is Sisyphus, the default orchestrator agent named after the Greek mythological figure condemned to roll a boulder up a hill for eternity.
“In greek mythology, Sisyphus was condemned to roll a boulder up a hill for eternity as punishment for deceiving the gods. LLM Agents haven’t really done anything wrong, yet they too roll their ‘stones’—their thoughts—every single day. My life is no different. Looking back, we are not so different from these agents.”
Inspired by Anthropic’s Ralph Wiggum plugin, the Ralph Loop is a self-referential development loop that runs until task completion:
Terminal window
/ralph-loop"Build a REST API"
The loop detects <promise>DONE</promise> to know when complete and auto-continues if the agent stops prematurely. It supports all programming languages and can be configured with custom iteration limits.
A: No! You can use Oh My OpenCode with just one provider. The plugin supports Claude, ChatGPT, and Gemini, but you pick what you have.
Q: Is this suitable for beginners?
A: The plugin is powerful but can be complex. The author recommends including ultrawork in your prompts — the agent will handle optimization automatically.
Q: How does this compare to AmpCode?
A: Oh My OpenCode is heavily influenced by AmpCode and Claude Code, with features ported and often improved. It’s designed for the open-source OpenCode platform.
Q: Can I use this with existing Claude Code configs?
A: Yes! The Claude Code compatibility layer means your existing configurations, hooks, and skills should work out of the box.
Oh My OpenCode represents the culmination of extensive real-world testing and refinement. It transforms OpenCode from a capable AI coding assistant into a sophisticated multi-agent orchestration platform.
Whether you’re a seasoned developer looking to maximize productivity or an AI enthusiast exploring the boundaries of what’s possible, Oh My OpenCode offers a powerful, extensible, and thoroughly tested solution.
As the author puts it:
“Stop agonizing over agent harness choices. I’ll do the research, borrow from the best, and ship updates here.”
Ready to supercharge your OpenCode experience? Install Oh My OpenCode and let Sisyphus keep that boulder rolling.
Claude Code has rapidly become a powerful AI-powered development companion, and its extensibility through plugins and MCP servers opens up incredible possibilities for specialized development workflows. Enter SivaLabs Marketplace - a curated collection of plugins designed specifically for Java and Spring Boot developers.
Created by K. Siva Prasad Reddy (sivalabs), a well-known Spring Boot author and educator, this marketplace brings enterprise Java development patterns directly into your AI-assisted coding experience.
SivaLabs Marketplace is a plugin collection for Claude Code that focuses on Spring Boot application development using the Java, Spring Boot, and Spring Data JPA technology stack. It provides specialized skills that automate common development tasks while following established best practices and architectural patterns.
The marketplace is currently in active development (as of January 2026) and includes the spring-boot-dev plugin as its flagship offering.
Creates the recommended package structure for Spring Boot projects following best practices. This skill ensures your project starts with a solid architectural foundation.
Automates the creation of JPA entities with proper annotations, relationships, and best practices. Simply describe your entity requirements, and the skill generates production-ready entity classes.
The marketplace also includes the context7 MCP server, which provides general library documentation lookup capabilities. This enhances Claude Code’s ability to reference official Spring documentation and other libraries during development.
SivaLabs Marketplace represents an exciting evolution in AI-assisted Spring Boot development. By combining Claude Code’s powerful AI capabilities with specialized Spring Boot knowledge, it helps developers work faster while maintaining code quality and following established patterns.
Whether you’re a seasoned Spring Boot developer looking to accelerate workflow or a learning developer wanting to see best practices in action, SivaLabs Marketplace is a valuable addition to your Claude Code toolkit.
As AI coding assistants become essential tools for modern developers, the ability to customize and streamline their behavior through slash commands has emerged as a powerful productivity booster. The slash-commands collection by regenrek offers a curated set of 13 battle-tested commands designed specifically for code review, problem analysis, and refactoring workflows. Let’s explore this comprehensive toolkit that works across Claude Code, Cursor, and Codex.
The slash-commands repository is a carefully curated collection of AI-powered slash commands created by Kevin Kern, a developer focused on building tools that enhance AI-assisted coding workflows. The collection addresses common pain points developers face when working with AI coding assistants:
Code review consistency - Standardized reviews across team members
Problem diagnosis - Systematic bug identification and impact analysis
Refactoring workflows - Structured approaches to code improvements
Planning and organization - Task management and documentation practices
Each command is designed as a Markdown template that can be invoked with a simple /command-name syntax, making complex workflows repeatable and shareable across teams.
Purpose: Identifies bugs and maps affected files across the codebase.
Usage:
/problem-analyzer "the app won't compile. here are the logs <paste-logs>"
Provides:
Root cause analysis
File impact assessment
Minimal safe fix proposals
Documentation gap identification
Pro tip: Works best when combined with error logs and a short problem description. Consider using with faster models like Cheetah for quick analysis, then switching to more capable models for detailed solutions.
When to use: During debugging sessions for faster issue resolution.
Kevin Kern (@kregenrek | kevinkern.dev) is the creator of the slash-commands collection and founder of instructa.ai, where he teaches developers how to build software with AI.
The slash-commands collection by regenrek transforms AI coding assistants from generic tools into personalized, team-oriented productivity systems. By providing 14 carefully crafted commands covering planning, review, debugging, refactoring, and optimization, this collection addresses real workflows that developers face daily.
Whether you’re working solo or part of a team, these commands help maintain consistency, catch issues early, and document decisions effectively. The beauty lies in their simplicity—Markdown files that can be customized, shared, and version-controlled alongside your code.
Start with /create-plan and /code-review-low, then gradually incorporate more commands as your team’s needs evolve. Your future self (and your teammates) will thank you for the investment in standardized, efficient workflows.
“Each unit of engineering work should make subsequent units easier—not harder.”
In traditional software development, every feature added makes the next feature harder to build. More code means more edge cases, more interdependencies, and more complexity. This accumulation of technical debt is the default trajectory for most codebases.
EveryInc’s compound-engineering-plugin for Claude Code inverts this paradigm. It implements a compound engineering methodology where each unit of work makes future work easier through a systematic learning loop.
The compound-engineering-plugin is an official Claude Code plugin developed by EveryInc that orchestrates AI agents to plan, write, review, and compound engineering knowledge. The plugin embodies a revolutionary approach to software development where:
80% of effort goes into planning and review - ensuring thorough understanding before code is written
20% goes into execution - leveraging AI agents for code generation
Knowledge compounds over time - each lesson learned feeds back into the system
The compound engineering approach requires a mindset shift. Traditional developers accustomed to “just coding” may find the emphasis on planning unfamiliar.
⚠️ Initial Investment
The first few cycles will feel slower than traditional development. This is normal - you’re building the knowledge foundation that future cycles will leverage.
⚠️ Tool Requirements
Full benefits require Claude Code with the plugin installed. The methodology is tool-agnostic in theory but optimized for this stack.
A: No. While Every uses it to enable solo developers to build complex products, teams can benefit even more from the systematic knowledge capture and multi-agent review process.
Q: Does this work with existing codebases?
A: Yes. The Plan phase includes researching your existing codebase, making it effective for both greenfield and brownfield projects.
Q: Can I use this with other AI coding tools?
A: The plugin is designed for Claude Code, but the compound engineering philosophy is tool-agnostic. Some Every team members also use OpenAI’s Codex CLI.
Q: What happens if the plan is wrong?
A: The Review and Compound phases capture why the plan didn’t work, feeding that learning back into future planning cycles.
Claude Plugins.dev:Community Registry - Discover more Claude Code plugins
Summary: The compound-engineering-plugin represents a paradigm shift in software development. By treating knowledge as a first-class artifact and systematically capturing learnings, it makes each unit of engineering work easier than the last. Whether you’re a solo developer or part of a team, this approach offers a path to sustainable, compounding productivity gains.
In the world of AI-assisted development, a fascinating technique has emerged from the mind of Geoffrey Huntley. Named after the famously persistent Ralph Wiggum from The Simpsons, the Ralph Wiggum technique embodies a simple yet powerful philosophy: keep trying until you succeed.
As Huntley famously describes it:
“Ralph is a Bash loop” - a simple while true that repeatedly feeds an AI agent a prompt file, allowing it to iteratively improve its work until completion.
Now, Anthropic has released an official Claude Code plugin that implements this technique, bringing autonomous AI development loops to your fingertips.
The Ralph Wiggum technique is built on the principle of persistent iteration despite setbacks. Instead of trying to get everything right in a single shot, you let the AI fail, learn from its failures, and try again - automatically.
The traditional approach to AI-assisted development looks like this:
Human → Ask AI → AI responds → Human reviews → Human asks again → ...
The Ralph Wiggum approach flips this:
Human → Define task with completion criteria
AI → Work → Try to exit → Block exit → See previous work → Try again
The loop happens inside your current session - no external bash loops needed. The Stop hook in hooks/stop-hook.sh creates the self-referential feedback loop.
/ralph-loop"Build a REST API for todos. Requirements: CRUD operations, input validation, tests. Output <promise>COMPLETE</promise> when done."--completion-promise"COMPLETE"--max-iterations50
The Ralph Wiggum plugin represents a paradigm shift in how we think about AI-assisted development. Instead of treating AI as a one-shot tool, it embraces the reality that software development is iterative - and AI should be too.
By letting Claude fail, learn, and try again automatically, we unlock a level of autonomy that wasn’t possible before. Whether you’re building a REST API, generating test suites, or working on a complex multi-phase project, Ralph Wiggum gives you the power to define the destination and let AI find the path.
In the rapidly evolving landscape of AI-powered development tools, one challenge has persisted: AI coding assistants often generate database code that is outdated, missing critical constraints, unaware of modern PostgreSQL features, and inconsistent with real-world best practices.
Enter pg-aiguide – an innovative MCP server and Claude plugin developed by Timescale (now Tiger Data) that provides AI-optimized PostgreSQL expertise to coding assistants.
pg-aiguide is an open-source tool (Apache-2.0 licensed) that helps AI coding tools write dramatically better PostgreSQL code. It provides three core capabilities:
Semantic Search across the official PostgreSQL manual (version-aware)
AI-Optimized Skills – curated, opinionated Postgres best practices used automatically by AI agents
Extension Ecosystem Docs, starting with TimescaleDB, with more extensions coming soon
The semantic_search_postgres_docs function performs semantic search over the official PostgreSQL manual, with results scoped to specific Postgres versions. This means AI agents get accurate, version-relevant information.
For more complex scenarios, like an IoT device monitoring system:
“Generate a Postgres schema for an IoT device company. Devices collect environmental data including temperature, humidity, pressure, and other measurements. Each device has a unique ID and human-readable name. Analysis includes finding outliers, anomalies, and device-specific ad-hoc analysis.”
pg-aiguide helps design schemas optimized for both recent data analysis and historical reporting.
pg-aiguide represents a significant step forward in AI-assisted database development. By providing AI agents with deep, versioned PostgreSQL knowledge and proven patterns, it bridges the gap between AI-generated code and production-quality database schemas.
Whether you’re a developer using AI coding assistants or a team building AI-powered development tools, pg-aiguide offers a practical solution to the persistent problem of suboptimal AI-generated database code.
Core Insight: Can Zhipu GLM-4.7 replace the expensive Opus 4.5 in the Claude Code environment? How does its 200K context window perform within the document-driven BMAD workflow? This post reveals the answers through the real-world development of the HeartPetBond app.
In the AI-assisted programming realm, Claude Opus 4.5 has been the undisputed king, but its high cost (approx. $15/1M input, $75/1M output) deters high-frequency usage. DeepSeek V3, while affordable, occasionally hits bottlenecks with ultra-long contexts (>128K tokens).
I aimed to verify Zhipu GLM-4.7, a strong challenger to DeepSeek V3, by developing the HeartPetBond app to test:
Long Context Capability: Can the nominal 200K Tokens Context support an entire project’s documentation and codebase?
Output Length: Can the 128K Tokens Output generate complex Flutter pages in one go?
Stability & Hallucination: Does logic crumble during continuous development over several days?
Cost-Effectiveness: How much money can it save independent developers compared to Opus 4.5?
Context: When using DeepSeek V3 with Claude Code + BMAD, I encountered errors like API Error: 400 ... maximum context length is 131072 tokens... when the context exceeded 128K. GLM-4.7’s 200K context might be the solution.
With over 10 years of Flutter experience and previous success generating the MoneyMind App using Claude Code + BMAD, I set out to see if this new model could handle the challenge.
PoC Phase Total: Approx. 300 Million (300M) tokens.
Estimated MVP: For 15 pages and App Store submission, estimated 3 Billion (3B) tokens.
Despite this massive volume, GLM-4.7’s cost is only about 1/7th of Opus 4.5. A Pro subscription (~¥200/month) essentially covers the MVP development needs.
High Cost-Performance: GLM-4.7 is a perfect “affordable alternative” to Opus 4.5. It significantly outperforms DeepSeek V3 in daily coding tasks while remaining wallet-friendly.
Long Window Stability: The 200K Context Window is real. It maintained clarity without “catastrophic forgetting” even after loading mass amounts of BMAD documentation.
Engineering Ready: Works seamlessly with Claude Code’s toolchain to handle complex refactoring and debugging.
Reasoning Minor Flaws: In very complex business flow designs, GLM-4.7’s reasoning chain occasionally misses a step compared to the meticulous Opus 4.5. This is manageable via human Code Review and clearer prompts.
Multimodal Gaps: Current API support for multimodal inputs (images/files) is limited, requiring an additional zai-mcp-server configuration.
If you are an independent developer looking for a smart AI pair programmer that won’t break the bank, Claude Code + GLM-4.7 + BMAD is the combination to beat right now.
Life is compounding. Start building your bond today.
Claude Code, Anthropic’s official CLI tool for AI-assisted development, has a powerful automation system through its .claude configuration files. However, creating complex workflows with multiple sub-agents, conditional branching, and user interactions requires editing markdown and YAML files manually. ClaudeCode Workflow Studio (cc-wf-studio) by breaking-brake brings a visual, drag-and-drop editor to VSCode, making workflow creation intuitive and accessible.
One of the standout features is AI-assisted refinement. Instead of generating workflows from scratch once, you can iteratively improve them through conversation:
Click the “Edit with AI” button (✨ sparkle icon)
Describe your changes in natural language (max 2000 characters)
The AI modifies your workflow incrementally, preserving existing work
Repeat until satisfied
This conversational approach builds workflows through multiple rounds of feedback, with the AI maintaining conversation history and applying changes incrementally.
Example prompts:
“Add a Sub-Agent node that validates the input data”
“Connect the error handler to a logging Sub-Agent”
“Change the AskUserQuestion node to have 3 options: High, Medium, Low”
ClaudeCode Workflow Studio bridges the gap between technical power and accessibility. By bringing visual programming to Claude Code workflows, it:
Lowers the barrier to entry - No need to understand markdown/YAML structure
Accelerates development - Visual feedback reduces trial-and-error
Enables collaboration - Teams can share and review workflows visually
Integrates seamlessly - Exports work directly with Claude Code CLI
As the Claude Code ecosystem continues to evolve, tools like cc-wf-studio demonstrate how community extensions can enhance the core platform’s capabilities.
When Mitchell Hashimoto, founder of Ghostty (the modern, GPU-accelerated terminal emulator), wanted to improve the macOS update feature, he didn’t just write code — he had a conversation. In a remarkable session he calls “vibe coding,” Mitchell paired with Claude to iteratively improve the codebase through dialogue, exploration, and shared discovery.
This isn’t about AI replacing developers. It’s about AI as a collaborative partner — and the transcript shows something beautiful: the AI catches things humans miss, the human provides direction and context, and together they build something better than either could alone.
Vibe coding is a conversational approach to software development where the developer and AI work together through natural dialogue. Rather than treating the AI as a command-execution tool, the developer treats it as a pair programmer — discussing, exploring, and iterating together.
In Mitchell’s session, captured from a real coding conversation on AmpCode, the flow goes like this:
Oracle Consultation — Ask Claude to analyze code for improvements
Discussion — Talk through the findings, make decisions together
Implementation — Write code with Claude’s assistance
Iteration — Learn from failures, adjust approach, try again
Testing — Verify changes work, add tests for confidence
Mitchell started with a simple request: “Are there any other improvements you can see to be made with the @macos/Sources/Features/ Update feature? Don’t write any code. Consult the oracle.”
The “oracle” — Claude in analysis mode — identified several categories of improvements:
What makes this session remarkable is the back-and-forth:
Mitchell: “Stop stop stop. Undo all the main actor stuff.”
Claude: “Done! I’ve reverted all the @MainActor changes. The build now succeeds…”
Mitchell: “For the error message, instead of truncating, isn’t there a SwiftUI standard way to do this?”
Claude: “You’re absolutely right! SwiftUI has .lineLimit() and .truncationMode() for this…”
The human provides course correction when the AI goes down a problematic path. The AI suggests better approaches when the human asks questions. They learn from each other.
The oracle caught a retain cycle and progress value bugs that weren’t obvious. Code review tools often miss these too — having an AI analyze the code with fresh eyes catches different classes of issues.
When the AI suggested marking everything @MainActor, Mitchell recognized this would cause cascading compilation issues and asked to revert. Human judgment about architecture and trade-offs remains essential.
Vibe coding isn’t about AI writing code for you. It’s about having a conversation with code — where the AI brings analytical superpowers, the human brings judgment and context, and together they create something better.
Mitchell’s session shows the future of programming: not humans replaced by AI, but humans augmented by AI — working together, learning from each other, and building better software through conversation.
The improvements to Ghostty’s update feature — safer code, better tests, improved accessibility — are a testament to what’s possible when developers embrace AI as a collaborative partner rather than a mere tool.
Claude Code has revolutionized AI-assisted development, but managing its extensive configuration files, chat history, and custom skills across multiple projects can be challenging. Enter Lovcode — a beautiful desktop companion application that brings all your Claude Code management needs into one unified interface.
Lovcode is a cross-platform desktop application built as a companion for AI coding tools, with primary focus on Claude Code. It provides a graphical interface to browse chat history, manage configurations, commands, skills, hooks, and more — all without touching the command line.
GitHub Repository:MarkShawn2020/lovcodeAuthor: Mark Shawn (@markshawn2020)
License: Apache-2.0
Platforms: macOS • Windows • Linux
Latest Version: v0.19.0
Browse and search through all your Claude Code conversations across projects with full-text search capabilities. Quickly find past solutions, code snippets, or discussions without digging through JSON files.
Configure and monitor Model Context Protocol (MCP) server integrations visually. Manage connections, settings, and status without editing configuration files.
Version 0.18.0 introduced command trend charts, showing your usage patterns over time. Track which commands you use most and identify opportunities for optimization.
Lovcode fills an important gap in the Claude Code ecosystem by providing a polished desktop interface for managing all aspects of your AI coding assistant. Whether you’re a solo developer looking to organize your configurations or part of a team establishing standards, Lovcode offers the tools you need in a beautiful, performant package.
Download it today from GitHub Releases and take control of your Claude Code workflow.
“Transform a PRD into a fully deployed, revenue-generating product with zero human intervention.”
What if you could turn a single product requirements document into a complete, deployed startup—without writing a single line of code yourself? Meet Loki Mode, a Claude Code skill that orchestrates 37 specialized AI agents across 6 swarms to autonomously build, deploy, and operate entire businesses.
Stop juggling AI accounts. Quotio is a beautiful native macOS menu bar app that unifies your Claude, Gemini, OpenAI, Qwen, and Antigravity subscriptions – with real-time quota tracking and smart auto-failover for AI coding tools like Claude Code, OpenCode, and Droid.
As developers, we increasingly rely on multiple AI coding assistants in our daily work. You might use Claude Code for intelligent code completion, GitHub Copilot for inline suggestions, Cursor for AI-powered IDE features, and various OpenAI-based tools for different tasks. Each service comes with its own:
Subscription and billing
API keys and authentication
Usage quotas and rate limits
Separate dashboard and management interface
This fragmentation creates constant overhead. You’re juggling multiple browser tabs, switching between different CLI configurations, and inevitably hitting rate limits on one service while another sits idle. Enter Quotio – a unified solution that brings all your AI subscriptions under one roof.
Quotio is a native macOS application that provides unified quota monitoring and request routing for multiple AI provider services. Created by developer Trong Nguyen (nguyenphutrong), Quotio serves two complementary purposes:
Visual Dashboard – Track all your AI API usage limits in one beautiful interface
Optional Local Proxy – Route requests across multiple provider accounts with intelligent load balancing and failover
Quotio solves a real pain point for developers who use multiple AI services. By providing unified quota monitoring, intelligent request routing, and seamless failover, it transforms the fragmented experience of managing multiple AI subscriptions into a cohesive, productive workflow.
Whether you’re an individual developer juggling personal AI subscriptions or part of a team with multiple provider accounts, Quotio offers the tools you need to maximize your AI productivity.
Creating engaging content for WeChat public accounts (公众号) is a time-consuming process that involves research, writing, headline optimization, and formatting. The wechat-article-writer skill for Claude Code automates this entire workflow, allowing content creators to produce professional articles in minutes rather than hours.
wechat-article-writer is a Claude Code skill from the happy-claude-skills collection that automates the entire WeChat public account article creation process. It handles:
Research - Multi-source web search for latest information
Writing - AI-powered content generation with storytelling elements
Headlines - Viral title generation with proven formulas
Formatting - Professional layout optimized for mobile reading
The wechat-article-writer skill transforms WeChat content creation from a multi-hour process into a streamlined workflow. By combining research, writing, headline optimization, and formatting into a single automated flow, it helps creators maintain consistency and quality while scaling production.
For Claude Code users who manage WeChat public accounts or create content in Chinese, this skill is a valuable addition to the toolkit.
Claude Code’s plugin system allows developers to extend its capabilities with custom skills. While the official marketplace is growing, the community has already started creating useful plugins. One such contribution is happy-claude-skills by iamzhihuix, a collection of three practical skills for common productivity tasks.
Claude Code’s plugin system enables community contributions that extend the tool’s capabilities beyond its core features. The happy-claude-skills collection demonstrates practical applications:
Document automation - Reduces manual formatting work
Media processing - Brings video handling capabilities into the coding workflow
Content creation - Streamlines article writing for Chinese audiences
As the Claude Code ecosystem grows, we can expect more community plugins to emerge, further enhancing the tool’s versatility.
When using Claude Code for software development, every session contains valuable insights — debugging approaches, architectural decisions, and problem-solving techniques. But these conversations are locked away in JSON files buried in ~/.claude/projects.
Simon Willison has released claude-code-transcripts, a Python CLI tool that converts these session files into beautiful, browsable HTML transcripts that can be shared with others.
claude-code-transcripts is a command-line tool that converts Claude Code session data (stored as JSON or JSONL files) into paginated, mobile-friendly HTML transcripts. It provides:
Better readability than the raw Claude Code interface
Shareable output that works with any static hosting or GitHub Gists
Multiple input sources: local sessions, web sessions via API, or direct JSON files
Clean, organized layout with pagination for long sessions
claude-code-transcripts fills an important gap in the Claude Code ecosystem by making sessions shareable and readable outside the Claude Code interface. Whether you’re documenting your work, sharing with colleagues, or building a knowledge base, this tool transforms ephemeral AI conversations into permanent, valuable resources.
Claude Code Board is a web-based Kanban-style management interface designed specifically for the Claude Code CLI. If you are a power user of Claude Code, you might find yourself juggling multiple terminal windows to handle different tasks concurrently. Claude Code Board solves this by providing a unified, visual interface to manage these sessions.
It aims to enhance the developer experience by allowing concurrent management of multiple AI coding sessions with real-time status tracking (idle, processing, completed, error).
Run multiple Claude Code instances simultaneously. The Kanban board allows you to see the status of each session at a glance—whether it’s idle, processing, or completed.
Group your sessions under “Work Items,” tag them by project or topic, and automatically manage workspace paths. This keeps your work organized conceptually, not just by terminal ID.
Imagine you are working on a feature, but you also need to fix a critical bug and review a PR. Instead of opening three terminals and losing track of context, you can create three cards on the Claude Code Board: “Feature X”, “Bug Y”, and “PR Review Z”. You can switch between them easily and see their progress.
You can configure specialized agents (e.g., an “Architect” agent or a “Debugger” agent) by creating .md files in your agents directory. When starting a session, you can select the specific agent best suited for the task.
Security: This tool is designed for local/personal use only. It does not have enterprise-grade security (encryption/auth) and should not be deployed to public networks.
Use Zhipu AI’s GLM models in Claude Code with the official GLM Coding Plan integration. A complete guide to installation, configuration, and daily usage.
Claude Code is an intelligent coding tool that runs in the terminal, helping developers quickly complete code generation, debugging, refactoring, and other tasks through natural language commands. But what if you want to use China’s powerful GLM models instead?
GLM Coding Plan is the official solution from Zhipu AI that integrates GLM models directly into Claude Code. With this integration, you can leverage models like GLM-4.7 and GLM-4.5-Air while keeping the familiar Claude Code workflow.
This guide covers everything you need to know: from installation to advanced configuration, troubleshooting, and best practices.
Tip: If you’re not familiar with Node.js and have Cursor installed, you can type Help me install Claude Code in Cursor, and it will guide you through the installation process.
A: GLM Coding Plan is primarily designed for terminal-based Claude Code. However, you can configure similar API settings in Cursor if it supports custom endpoints.
Q: What’s the difference between GLM-4.7 and GLM-4.5-Air?
GLM Coding Plan brings the power of Zhipu AI’s GLM models to Claude Code, offering a compelling alternative for developers who prefer China-hosted models or need better Chinese language understanding. With proper configuration and model selection, you can achieve productivity gains while maintaining data locality.
The integration is straightforward, the documentation is comprehensive, and the official support from Zhipu AI ensures a smooth experience. Whether you’re building new features or debugging existing code, GLM Coding Plan has you covered.
What if Claude Code could enforce engineering discipline instead of just writing code? That’s exactly what obra/superpowers delivers—a complete software development workflow system built on composable “skills” that automatically activate to guide your development process.
With over 11,800 stars and 960 forks on GitHub, Superpowers has become the go-to framework for developers who want their AI coding assistant to follow systematic engineering practices rather than ad-hoc code generation.
The Core Philosophy: Engineering Discipline Over Quick Fixes
Skills trigger automatically based on context—no manual commands needed. The agent checks for relevant skills before any task, making these mandatory workflows, not suggestions.
Superpowers represents a paradigm shift from “AI writes code” to “AI follows engineering discipline.” By making structured workflows mandatory rather than optional, it produces higher-quality code through autonomous but systematic development processes.
If you’ve been frustrated by AI assistants that generate code without proper testing or design consideration, Superpowers is your solution. Install it today and transform Claude Code into the disciplined engineering partner you deserve.
Ready to level up your Claude Code experience?
Terminal window
/pluginmarketplaceaddobra/superpowers-marketplace
/plugininstallsuperpowers@superpowers-marketplace
Your future self (and your test suite) will thank you.
CodexBar is a tiny macOS 15+ menu bar app created by Peter Steinberger (@steipete) that keeps your AI coding assistant usage limits always visible. Monitor both OpenAI Codex and Claude Code limits - including 5-hour session windows and weekly caps - without logging into any dashboard.
Dual provider support: Track both Codex (5h/weekly + credits) and Claude Code (session/weekly + Sonnet-only limits) simultaneously
Zero login required: Reads directly from local CLI output - no passwords, no browser sessions needed
Real-time visibility: See remaining tokens, reset timers, and account info from your menu bar
If you’re actively using Claude Code or OpenAI Codex for development, you’ve likely hit rate limits unexpectedly. Here’s why CodexBar is essential:
Avoid workflow interruptions
Know your remaining quota before starting a major coding session. No more “rate limited” surprises in the middle of complex refactoring.
Track multiple time windows
Both services use overlapping 5-hour and weekly windows. CodexBar visualizes both so you understand exactly when limits reset.
Monitor Sonnet-only limits (Claude)
Claude Code’s Sonnet model has its own weekly cap. CodexBar shows this separately so you can strategically choose when to use which model.
Credits visibility (Codex)
See your paid credits balance alongside rate limits. Know when you’re burning through free tier vs. paid usage.
CodexBar solves a real pain point for heavy AI coding assistant users:
✅ See both Codex and Claude Code limits at a glance
✅ Monitor 5-hour sessions AND weekly windows
✅ Track Sonnet-specific limits and paid credits
✅ Zero login required - reads local CLI data
✅ Privacy-first design with no disk scanning
Install it via brew install --cask steipete/tap/codexbar and never be surprised by rate limits again.
📌 Want more Claude Code tips, usage optimization strategies, and AI developer tools? Follow our channel for the latest guides on maximizing your AI-assisted development workflow!
Behind every revolutionary product is a story of personal growth, strategic thinking, and relentless curiosity. Boris Cherny, the creator of Claude Code at Anthropic, exemplifies this truth. His journey from startups to venture capital, through a decade at Meta (Facebook/Instagram), to leading one of the most impactful AI developer tools demonstrates key principles that any engineer can apply.
This article distills Boris’s career insights, revealing how he went from a mid-level engineer to principal at Meta, and finally to creating Claude Code—a tool that now writes 80-90% of its own codebase.
Boris started his first startup at 18, worked at a hedge fund, and notably has no CS degree—he studied economics before dropping out to pursue startups. His trajectory proves that programming is a practical skill learned on the job, not in classrooms.
Key milestones at Meta (2012-2022):
IC4 (Mid-level): Joined “under-leveled” working on Messenger-Groups integration
IC5-IC6: Led Groups web migration; created the Undux state management library
IC7 (Senior Staff): Drove Public Groups feature with complex data model migrations
IC8 (Principal): Scoped work for hundreds of engineers; led Instagram’s Python-to-Hack migration
In the fast-evolving AI landscape, don’t build for current model capabilities—design for future ones.
“Don’t build for today’s model, build for the model six months from now.” — Ben Mann, Boris’s manager at Anthropic
This forward-thinking approach explains why Claude Code initially felt underwhelming but became transformative after Claude 4 series launched. Early products may have mediocre experiences, but when new models drop, you achieve quantum leaps.
The most successful products don’t invent new behaviors—they satisfy hidden, existing ones.
Boris observed at Facebook that users were “abusing” Groups for commerce 40% of the time. This observation birthed Facebook Marketplace. Similarly, Dating emerged from observing how people were already using the platform for connections.
Lesson: Watch what users are hacking together with your existing features—that’s where product-market fit hides.
High-impact engineers don’t just write code—they develop product sense, design ability, and user research skills.
At both Meta and Anthropic, there are no formal titles. Everyone is a “Member of Technical Staff” regardless of whether they’re an engineer, PM, or designer. This culture forces constant re-validation through execution rather than title.
“Just because you’re a product engineer doesn’t mean you can’t build infra.”
Claude Code started as an accidental discovery during internal experiments at Anthropic. Boris’s manager pushed beyond autocomplete thinking, encouraging design for future model capabilities.
After model improvements, usage jumped to 50% of coding work. Today, 80-90% of Claude Code is written by Claude Code itself. Surprisingly, adoption spread beyond engineers to data scientists and sales teams—unexpected use cases the team hadn’t anticipated.
Stop chasing titles—pursue interesting problems and impact. Promotions follow naturally.
Invest in side projects—even small automations that help your team compound into massive influence over time.
Become the generalist—learn product, design, and user research alongside engineering. The most valuable engineers are those who can wear multiple hats.
Apply common sense liberally—when lost in organizational complexity, ask “what does the user actually need?”
Build for the future—whether in AI or any fast-moving field, design for where technology will be, not where it is today.
Boris Cherny’s career offers a masterclass in engineering growth: start by solving your own problems, scale solutions to help others, earn trust through execution rather than titles, and always design for the future.
Claude Code exists because Boris spent a decade building products (many failed), learning what great developer experience feels like, and positioning himself at the intersection of code quality and AI capability.
For engineers seeking rapid career growth, his formula is clear: Be curious. Build side projects. Think in systems. Design for tomorrow.
When working with Claude Code on complex projects, you’ve likely encountered the frustrating “context degradation” problem. After several compaction cycles, you’re essentially working with a summary of a summary of a summary. Signal degrades into noise, and Claude starts hallucinating context.
Continuous Claude solves this fundamental problem through a revolutionary approach: clear, don’t compact.
Traditional approaches try to optimize compaction algorithms. Continuous Claude takes a fundamentally different path: preserve state externally, clear context completely, resume with full signal.
Continuous Claude represents a paradigm shift in how we approach context management for AI coding assistants. By embracing “clear, don’t compact” and building robust state preservation through ledgers and handoffs, it enables:
Lossless context preservation across sessions
Token-efficient MCP execution without context pollution
Intelligent agent orchestration with isolated context windows
Continuous learning that improves over time
For teams working on complex, long-running projects with Claude Code, Continuous Claude is an essential addition to your development toolkit.
Right now, the most powerful AI setups are being built inside companies for efficiency and profits. But what if you could have the same sophisticated AI infrastructure available to everyone? That’s exactly what Personal AI Infrastructure (PAI) delivers.
Created by security researcher and AI pioneer Daniel Miessler, PAI is an open-source template for building your own AI-powered operating system. It’s the foundation for creating a Personal AI System that understands your larger goals and context, gets better over time, and works for you because it’s yours.
PAI (Personal AI Infrastructure) is open-source scaffolding built on Claude Code, designed to be platform-independent. The architecture, skills, and workflows are structured so future migrations to other AI platforms remain straightforward.
“The best AI in the world should be available to everyone.”
The purpose of technology is to serve humans—not the other way around. PAI enables regular people to use AI to help them flourish, not just tech workers inside corporations.
Build the scaffolding that makes AI reliable before adding complexity. Following Miessler’s “solve once, reuse forever” philosophy, each component becomes reusable.
No! PAI is designed to give regular people access to sophisticated AI infrastructure. While technical knowledge helps, the goal is human flourishing, not just developer productivity.
Yes. While currently built on Claude Code, PAI’s architecture is platform-independent. The skills, workflows, and patterns can migrate to other AI platforms.
Q: How does PAI differ from just using Claude Code?
PAI adds structure, history, skills, and patterns on top of Claude Code. It’s not a different AI—it’s a framework that makes Claude Code work specifically for you.
Personal AI Infrastructure represents a paradigm shift in how we think about AI assistance. Instead of relying on generic chatbots or corporate AI systems, PAI empowers individuals to build AI infrastructure that:
Knows your context: Your goals, preferences, and working style
Learns over time: History compounds, patterns emerge
Serves your interests: Not engagement metrics or corporate profits
Remains yours: Open-source, portable, and customizable
As Daniel Miessler points out, the real power of AI infrastructure lies not in model intelligence but in orchestration. The magic happens when you create a system that knows you, adapts to your needs, and works autonomously on your behalf.
Start clean. Start small. Build the AI infrastructure you need.
AI coding agents have revolutionized software development, but each agent operates in its own ecosystem with unique capabilities. OpenSkills bridges this gap by bringing Anthropic’s powerful skills system to all AI coding agents.
OpenSkills is a universal skills loader that implements Anthropic’s Agent Skills specification for any AI coding agent. It brings Claude Code’s skills system to Cursor, Windsurf, Aider, and other agents with 100% compatibility.
OpenSkills democratizes AI agent skills by making Anthropic’s well-designed skills system available to all agents. Whether you’re using Claude Code, Cursor, Windsurf, or Aider, you can now benefit from:
AI coding assistants have become indispensable tools for developers, but their capabilities are often limited to what comes built-in. SkillsMP changes this by providing an open marketplace where developers can discover, install, and share skills across multiple AI platforms.
SkillsMP is an open-source marketplace hosting over 31,000 agent skills that work seamlessly with the most popular AI coding assistants:
Claude Code - Anthropic’s official CLI tool
Codex CLI - OpenAI’s command-line interface
ChatGPT - The world’s most popular AI assistant
All skills use the unified SKILL.md format, ensuring compatibility across platforms and making it easy to share capabilities between different AI tools.
Skills are model-invoked, not user-invoked. This means the AI assistant decides when to activate a skill based on the context of your request, rather than requiring explicit commands.
---
name: my-skill
description: What this skill does and when to use it
Claude Code supports running multiple skills simultaneously. The AI intelligently selects which skills to activate based on context, enabling complex workflows that combine several capabilities.
SkillsMP democratizes access to AI agent capabilities by providing:
31,000+ ready-to-use skills across 13 categories
Cross-platform compatibility via the SKILL.md standard
Open-source transparency with community quality control
Easy installation for all major AI coding assistants
Whether you’re looking to enhance your AI coding workflow or contribute your own skills to the community, SkillsMP provides the platform to make it happen.
In the rapidly evolving landscape of AI development, context engineering has emerged as one of the most critical disciplines for building effective agent systems. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the holistic curation of all information entering a model’s limited attention window.
Agent Skills for Context Engineering by Murat Can Koylan is a comprehensive, open-source collection designed to teach the art and science of curating context to maximize agent effectiveness across any platform.
Context engineering is the discipline of managing the language model’s context window. The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics.
Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes. This requires understanding what context is, how it degrades, and how to optimize it.
Extract the principles and patterns from any skill and implement them in your agent framework. The skills are deliberately platform-agnostic, making them applicable to any LLM-based system.
Each skill is structured for efficient context use. At startup, agents load only skill names and descriptions. Full content loads only when a skill is activated for relevant tasks.
2. Platform Agnosticism
These skills focus on transferable principles rather than vendor-specific implementations. The patterns work across Claude Code, Cursor, and any agent platform that supports skills or allows custom instructions.
3. Conceptual Foundation with Practical Examples
Scripts and examples demonstrate concepts using Python pseudocode that works across environments without requiring specific dependency installations.
Q: Do these skills work with any LLM?
A: Yes, the principles are LLM-agnostic. Examples use Python pseudocode that can be adapted to any environment.
Q: How do I know which skill to use?
A: Start with fundamentals, then move to architectural skills for design decisions, and operational skills for optimization.
Q: Can I contribute new skills?
A: Absolutely! Follow the template structure and submit pull requests. Community contributions are welcome.
Q: Are there costs associated with using these skills?
A: No, the repository is MIT licensed. However, the LLMs you use with these skills may have associated costs.
Q: How do skills handle context limits?
A: Progressive disclosure means only relevant content loads. Each skill is designed to stay under 500 lines.
Context engineering is becoming as fundamental to AI development as software engineering principles are to traditional development. Agent Skills for Context Engineering provides a structured, open, and platform-agnostic foundation for mastering this critical discipline.
Whether you’re building your first agent or optimizing production multi-agent systems, these skills offer the conceptual foundation and practical guidance needed for success. The progressive disclosure design means you can start small and expand as needed, while the platform-agnostic approach ensures your learning transfers across tools and frameworks.
The AI agent ecosystem is maturing rapidly, and context engineering will be a key differentiator between agents that work and agents that work well. Start with the fundamentals, understand degradation patterns, and build systems that manage context effectively.
Anthropic has released several major upgrades to Claude Code that fundamentally transform how developers interact with AI-powered development tools. These updates include a native VS Code extension, version 2.0 of the terminal interface, and most notably, checkpoints for autonomous operation. Powered by Claude Sonnet 4.5, Claude Code now handles longer, more complex development tasks with unprecedented confidence and control.
This article provides a comprehensive guide to these new autonomy features, covering installation, configuration, best practices, and real-world workflow examples.
Q: Can I use checkpoints without git?
A: Yes, but it’s not recommended. Checkpoints supplement, not replace, version control.
Q: How many subagents can run in parallel?
A: There’s no hard limit, but resource constraints apply. Start with 2-3 and scale based on your system.
Q: Do hooks slow down development?
A: Minimal overhead. Hooks run asynchronously where possible.
Q: Can I use Claude Code with local models?
A: The Agent SDK supports Bedrock, Vertex AI, and Foundry. Local model support depends on provider compatibility.
Claude Code’s autonomy upgrades represent a significant leap in AI-assisted development. The combination of checkpoints, subagents, hooks, and background tasks creates a development environment where you can confidently delegate complex tasks while maintaining full control. Whether you’re refactoring a legacy codebase, building new features, or exploring experimental approaches, these tools provide the safety net and parallel processing power to work more efficiently than ever before.
Start with the VS Code extension for a gentler learning curve, then graduate to the Agent SDK for building production-ready autonomous agents tailored to your specific workflows.
Ever wondered what makes Claude Code tick? Thanks to the open-source community, specifically the Piebald-AI/claude-code-system-prompts repository, we now have unprecedented visibility into the intricate system prompts that power one of the most popular AI coding assistants on the market.
This repository, maintained by the team behind Piebald, provides a comprehensive breakdown of all Claude Code’s various system prompts, updated with each new version. As of Claude Code v2.0.75 (December 20th, 2025), it covers 56 versions of changelog since v2.0.14.
Hugging Face Skills is a revolutionary system that enables AI coding agents like Claude Code, OpenAI Codex, and Google Gemini CLI to fine-tune open-source language models end-to-end through natural language conversations.
Instead of writing complex training scripts, configuring GPU resources, and managing deployment pipelines manually, you simply describe what you want in plain English:
Fine-tune Qwen3-0.6B on my customer support dataset for 3 epochs.
The AI agent handles everything else: data validation, hardware selection, script generation, job submission, progress monitoring, and model publishing.
Hugging Face Skills represents a paradigm shift in how we approach model fine-tuning. By bridging the gap between natural language and machine learning infrastructure, it democratizes access to custom AI model creation.
Whether you’re a developer looking to create a specialized code assistant, a business building a customer support bot, or a researcher exploring new training methods, HF Skills provides an accessible, cost-effective, and powerful solution.
Get Started Today:
Terminal window
# Install the skill
/pluginmarketplaceaddhuggingface/skills
/plugininstallhf-llm-trainer@huggingface-skills
# Authenticate
hfauthlogin
# Start training!
"Fine-tune Qwen3-0.6B on my dataset for instruction following."
The future of AI model training is conversational. Welcome to the age of Skills.
n8n-skills is a collection of 7 complementary Claude Code skills designed to teach AI assistants how to build production-ready n8n workflows using the n8n-mcp MCP server.
Created by Romuald Członkowski from AI Advisors, these skills solve the common challenges developers face when building n8n workflows programmatically.
n8n-skills transforms the way you build n8n workflows with Claude Code:
Automatic activation - Skills trigger when relevant queries are detected
Composable design - All 7 skills work together seamlessly
Production-ready patterns - Based on real-world usage
Validation first - Catch errors before deployment
Whether you’re building simple webhook integrations or complex AI agent workflows, n8n-skills provides the guidance you need to build flawless automation.
BMAD (Build More, Architect Dreams) is an AI-driven agile development methodology framework that integrates multiple AI Agents, workflows, and modules. It supports 18+ IDEs including Claude Code, Codex, and Cursor. This guide walks you through the complete installation and configuration process for BMAD v6.0.0-alpha.
Remember the shock when ChatGPT was first released? In that moment, AI moved from the lab into millions of homes.
Now, Anthropic is creating its own “ChatGPT moment”—not through a chat box, but via the Claude Chrome Extension, turning AI into a true “digital employee” within your browser.
The Claude Chrome Extension brings the powerful capabilities of Claude Code to ordinary users—you don’t need to know how to code or understand APIs. Just tell Claude what you want to do in your browser, and it will do it for you.
This is more than just a browser plugin; it’s a significant step toward integrating AI into daily workflows.
ChatGPT’s success lay in making AI conversations accessible to everyone. The Claude Chrome Extension does the same, but in a different direction:
From Conversation to Action: While ChatGPT taught people to talk to AI, Claude allows AI to actually do things for you.
From Developers to Everyday Users: The power of Claude Code is now available to everyone.
From Standalone Tools to Global Assistants: It’s no longer just a web app, but an integrated part of the entire browsing experience.
This is precisely the breakthrough Anthropic needs—using a differentiated product experience to carve out a new track in a market dominated by ChatGPT.
The Claude Chrome Extension represents a new direction in AI development:
Beyond Chat: True task execution.
Beyond Developer Tools: Accessible to everyone.
Beyond Feature Bloat: A revolution in workflow.
For Anthropic, this could be the key step in shifting the market landscape. While ChatGPT remains far ahead in user numbers, Anthropic is catching up in a different way—deeper enterprise integration and closer alignment with actual work.
In the second half of the AI competition, it’s no longer just about whose dialogue is smarter, but who can actually help users get things done.
What if you could go from a simple idea to a fully researched, professionally written article published to your WeChat Official Account - all in minutes, without leaving your terminal?
This isn’t science fiction. With Claude Code and the wenyan-mcp (Model Context Protocol) server, you can create an end-to-end content automation pipeline that:
This workflow transforms content creation from a tedious multi-step process into a streamlined, automated pipeline. With Claude Code + wenyan-mcp, you can:
Save hours on research and writing
Maintain consistency across bilingual content
Publish directly without copy-paste gymnastics
Focus on ideas rather than execution
The future of content creation is here - and it lives in your terminal.
Approximately 30,000 Accenture professionals that will be trained on Claude, including forward deployed engineers (also known as “reinvention deployed engineers” at Accenture) who help embed Claude within client environments to scale enterprise AI adoption. This will comprise one of the largest ecosystems of Claude practitioners in the world. These teams combine Accenture’s AI, industry, and function expertise—along with deep partnerships with leading cloud providers—with Anthropic’s Claude models and Claude Code, plus its proven playbooks for regulated industries.
[[Methodology]]
quantify real productivity gains and ROI
workflow redesign for AI-first development teams
change management and training that keeps pace as AI evolves
[[Customization]]
Anthropic and Accenture will also co-invest in a Claude Center of Excellence inside Accenture, creating a dedicated environment for the joint design of new AI offerings tailored to specific enterprise needs, industry requirements, and regulatory contexts.
Yesterday Anthropic published a new article explaining how their legal team uses Claude / Claude Code to compress work that used to take days into a few hours—while keeping humans in the loop the whole time.
1. Even at a top AI company, legal is still squeezed by grunt work
On the night before launch, marketing would dump landing pages, push copy, and posters on legal: “Please review today; we go live tomorrow.”
Legal had to open the docs -> read end to end -> leave a pile of comments -> go back and forth for multiple rounds. It often dragged on for days.
Anthropic’s rebuild is simple but ruthless:
They built a Self-Review Tool in Slack:
Marketing pastes the copy into it
Claude calls a dedicated legal Skill file
It contains: past review comments, common risk points, and the team’s judgment criteria
Claude automatically flags:
Exaggerated claims?
Is there a source for the data?
Any violation of portrait rights or reputation?
Any implication of promises that should not be made?
Assigns a risk level to each issue: Low / Medium / High
Also provides a suggested revision
Only after passing this gate will a ticket be created and enter the legal queue.
StartupHub reported that with this flow, reviews that took days now compress to a few hours, and Slack can auto-generate a summary to help legal see the risk points clearly.
The essence:
Let AI handle the “intake form” and “basic checkup.” The true diagnosis still belongs to the doctor.
2) Side gig / external activity approval: AI asks for details first
If an employee wants to moonlight as an adviser, pursue a side business, or serve on a nonprofit board, they must go through “external business activity approval.”
Previously, legal would ask one-on-one about background, conflicts of interest, time commitments, and non-compete risks—going back and forth endlessly.
Here is how Anthropic runs it now:
The employee fills out a structured form
Claude reads the form
If information is missing, it will automatically ask follow-up questions
Based on company rules, it provides a recommended decision + risk explanation
It then enters the legal queue for an attorney to confirm approval or rejection
Lawyers now mainly focus on two buckets:
True edge cases
Higher-risk borderline situations
They separated “90% standard flow + 10% extreme judgment.”
3) Privacy impact assessments (PIA): teach AI to write the template
After discussing Anthropic, let’s talk about borrowing the idea. Whether you use Claude, ChatGPT, DeepSeek, or your own model, you can follow the same playbook.
Is the automation worth it? Anthropic’s “How Anthropic teams use Claude Code” notes that many teams measure efficiency this way, and legal shows especially clear returns.
You can quantify quickly:
Average review time per marketing asset
Cycle time from “sent to legal” to “legal response” for each contract
Hours per week each attorney spends on repetitive documents
Do a before-and-after, and it becomes easy to justify more resources.
It automatically records what you do in Claude Code, summarizes and compresses it,
and the next time you open the same project, Claude will “actively recall” past context.
More specifically, it is:
A Claude Code plugin
Automatically captures: your prompts, tool calls, file reads/writes, etc.
Uses Claude’s Agent SDK in the background to summarize and extract key points
Stores them in a local database and vector store; supports full-text + semantic search
When you reopen Claude Code, it injects the most relevant history
back into the new conversation automatically
So the experience is:
You keep coding, and Claude becomes the partner
who remembers the project background even better than you do.
Each time you start a new session in Claude Code,
claude-mem digs out “recent key info” from the database,
organizes it into a concise context block,
and injects it automatically at the top of the conversation.
It’s like running through, in your head, before meeting a friend:
“Last time we talked about login and signup—what pitfalls did we hit?“
Condenses the entire session into a “project changelog + decision digest.”
That gets stored in local SQLite (for full-text search)
while also being written to a vector database like Chroma for semantic search.
Next time Claude looks up history, it won’t rummage through everything.
It uses smart search + progressive disclosure to inject
a small but crucial slice of context back into the chat.
“That file upload bug—how did we fix it back then?”
Unlike a basic code keyword search,
mem-search looks through conversations + tool calls + summarized knowledge, and
lets you control how much context to inject via progressive disclosure, saving tokens.
Simply put:
It turns “I vaguely remember we discussed this” into “I can surface it on demand.”
3) Web UI: see what Claude is “thinking” in real time
This year, every developer friend around me keeps asking the same question:
“I use Claude/Copilot every day, but is it really worth it? Are the vendors overselling me?”
Coincidentally, Anthropic turned the real usage data of 132 internal engineers and researchers into a detailed survey and interviews, plus several charts.
It is like an AI vendor giving itself a physical exam. Who is delighted, who is stumbling—everything is written in the numbers.
Let’s look at the report from a working engineer’s perspective and talk about:
In software engineering, which tasks have the highest ROI with AI, and which ones should you not rush to offload to a model?
Anthropic asked 132 engineers and researchers a series of very concrete questions:
How much of your daily work do you do with Claude?
What do you mainly use it for? Debugging, new features, refactoring, tests, documentation, or design and planning?
For each task type, does working with Claude make you spend more or less time?
Does the volume of output (code, docs, deliverables) go up or down?
They also compared changes over time:
A year ago, employees estimated that about 28% of their work used Claude, self-reporting a +20% productivity lift.
Now that number has risen to 59% of work done with Claude, with an average self-reported productivity +50%; around 14% of “power users” believe they improved by over 100%.
Another independent study is also striking: using 100,000 real conversations, Anthropic estimated that the median task completion time dropped by roughly 80%.
It sounds dramatic, but engineers know self-reported productivity needs a haircut.
The more useful takeaway is: by task type, where are the real gains and where is the fluff?
II. Overall Pattern: Time Saved Is Small, Output Gain Is Big
Frontend: run it and you immediately see if the UI is right.
Debugging: reproduce the bug, run tests—pass or fail.
Tests and docs: format and coverage are obvious at a glance.
Task boundaries are clear
“Make this component responsive.”
“Add a set of unit tests for this code.”
“Write a README for new teammates.”
In these jobs, the most annoying part for humans is:
mechanical, repetitive, boring to write, yet quality is visible instantly.
This matches a keyword Anthropic staff highlighted:
“I’m more willing to hand Claude tasks that are easy to verify.”
The report also notes that engineers increasingly use Claude for frontend visualization, implementing UI, and building small tools,
even encouraging backend engineers to write frontend and become “quasi full stack.”
From an ROI lens, these tasks are short, fast projects:
Model does the work
Human reviews
If it breaks, roll back and redo
Beginners can supervise
Frontend is a clear beneficiary—screens, interactions, styles iterate quickly.
You focus on user experience itself instead of wrestling with CSS details.
2) Lower ROI: Design and Planning, Infrastructure/DevOps
Run them and see whether the result is in the expected range
Tasks relying on “taste” and “judgment” need human oversight
System design, UX design, security strategy
Often no standard answers, only trade-offs
Local problems vs. global problems
Changing one component or writing a doc is a local problem.
Planning a cross-team technical roadmap is a global problem.
Claude delivers huge ROI on local, verifiable tasks.
On global, fuzzy tasks, it is better for brainstorming, lists, and comparisons,
not for directly giving the final plan.
V. Practical Tips for Typical Teams: Don’t Rush to Make AI the Architect
This technical blog is distilled from a real production incident investigation. It focuses on how Claude Code “explores → thinks → reasons” to quickly pinpoint the root cause, ship the smallest viable fix, and propose follow‑up structural improvements.
The problem occurred in the mailflow service in production. The core error was:
This Session's transaction has been rolled back due to a previous exception during flush.
To begin a new transaction with this Session, first issue Session.rollback().
Original exception was: (sqlalchemy.exc.InvalidRequestError) Can't reconnect until invalid transaction is rolled back
From the surrounding logs, the failure happened when executing an INSERT into the emails table. Subsequent database operations then entered a “cascade failure” state.
Leveraging Claude Code’s code search and cross‑context capabilities, we proceeded “from symptoms to root cause” in order:
Locking onto logs and trigger point
The logs show the exception stems from the SQLAlchemy Session state: after a prior transaction failure, rollback() was not performed, leaving the current Session invalid.
Locating key code paths
mailflow/app/db.py: connection and session factory definitions.
mailflow/app/mail.py: main business flow, record creation, and commit points.
mailflow/app/utils.py: logging and WeCom notification entry notify_and_log.
Evidence 1: Module‑level global Session
At the top of mail.py there is:
session =session_factory() # module‑level global Session
Once any flush/commit fails, this global Session becomes invalid, and all subsequent DB operations that rely on it will fail until an explicit session.rollback() is issued.
Evidence 2: Missing exception handling and rollback
Relating attachments to mail (_relation_attachment_and_mail)
Updating status when dealing with request results (deal_requests_result)
Large sections of the main flow in process_mail
Conclusion: The first failed DB operation puts the global Session into an invalid state. Because rollback() is missing, all later commits and even queries on that path fail with the same error, forming a “cascade failure”.
3. Reasoning Chain: From Hypothesis to Verification
Hypothesis: An INSERT/UPDATE exception (possibly from data constraints, transient network issues, or the pool) invalidates the global Session; without rollback(), subsequent steps (status updates, attachment relations, mail moves) all fail.
Code verification:
Found the global session = session_factory();
Searched for session.commit() call sites — most lack except: session.rollback();
This matches the error semantics exactly.
Strategy: Move fast with minimal change to restore stability first, then evaluate structural refactoring.
P0 (immediate, smallest change): add session.rollback() in exception handlers so the Session can self‑recover.
P1 (comprehensive guard): wrap every commit() with try/except/rollback to avoid any single failure taking down the rest of the flow.
P2 (structural): replace the module‑level global Session with scoped_session to isolate session state across processing flows (recommended, not mandatory for the hotfix).
Note: scoped_session provides better isolation in concurrent/multithreaded environments and reduces cross‑request contamination. The production hotfix first ensures rollback semantics are correct and complete; then evaluate the migration cost/benefit.
Anti‑pattern: a module‑level global Session, once invalid, causes chain‑wide cascade failures.
Principle: every commit() should be wrapped by try/except + rollback; also rollback() in the top‑level exception handler of the main flow.
Design: adopt a failure‑resilient session model (scoped_session or acquire/release per processing unit).
Operations: log and alert immediately (this project uses WeCom via notify_and_log), and include key identifiers (uid, subject, time, to_list) in the message.
Have you had this moment? You grind on reports like brick‑laying or pull all‑nighters on slides, but a teammate seems to have a cheat code—one prompt and they ship a brand‑compliant pack.
The newly released Claude Skills is how to install that “cheat” into Claude. It wraps repetitive work into modules that auto‑match and auto‑run—like “expertise packs” on demand.
One folder = one skill. Inside are instructions (SKILL.md), scripts, and resources. Claude auto‑loads and uses the skill for fitting tasks. You don’t need to code to turn your work patterns into reusable skills.
Quick highlights
– Auto trigger: semantic matching to the right skill
– Composable: multiple skills can collaborate
– Code execution: run scripts when needed, stable and reproducible
– Works everywhere: Claude.ai, Claude Code, and API
– Enterprise‑ready: brand rules, compliance checks, spreadsheet automation
How it “automates”: 5 moments from discovery to collaboration
In short, Claude Skills productize your workflow. Package experience, rules, and templates into triggerable, reusable, and composable skills. Install a “report skill” today, add a “brand skill” tomorrow—and your AI really will learn your way of working.
Which task would you skill‑ify first? Share in the comments.
When teams start collaborating with AI coding assistants, clearly answering “what to build, how to build it, and how we know it’s done” becomes crucial. This guide walks you through a practical, reviewable, and traceable development workflow using OpenSpec (documentation‑driven development) together with Claude Code (an intelligent coding assistant).
Who this is for:
Teams that want specs before changes in multi‑person collaboration
People/teams that want AI assistants embedded into a standard workflow
Projects that require approval, archiving, or compliance control
OpenSpec provides project context and process standards; Claude Code executes the Read → Generate → Implement → Verify cycle. After initialization, start your Claude Code session with prompts like these:
Fill in project context:
Please read openspec/project.md. Based on the repo’s current state, help me fully document project goals, tech stack, conventions, external dependencies, constraints, and naming standards.
Create the first change proposal:
I want to add “[your feature]”. Please create an OpenSpec change proposal (changes/[id]/) including proposal.md, tasks.md, and the relevant specs delta under changes/[id]/specs/.
Explain/enforce the workflow:
Using openspec/AGENTS.md, explain the three‑phase workflow and strictly follow it in subsequent steps: propose and validate first, implement after approval, archive when complete.
Tip: After installation, Codex/Windsurf/Cursor, etc. will also pick up the same guidelines so multiple assistants collaborate consistently.
Use OpenSpec to make Requirements → Design → Tasks → Changes explicit from day one and treat them as the project’s source of truth. Then leverage Claude Code for efficient execution and collaboration so AI delivers within clear boundaries. The benefits:
Specs first to reduce rework; approvals up front to lower risk
Task closure with traceable outputs; archivable changes for reusable knowledge
Today’s update is very kind to developers who code with Claude Code.
Claude Haiku 4.5 launches — cheaper and faster; Plan mode defaults to Sonnet for planning and switches to Haiku for execution.
In practice, projects run smoother and your wallet hurts less. Official pricing/positioning and how this release hooks into Claude Code both support that conclusion.
Haiku 4.5: $1 / $5 (input/output per million tokens) — aimed at real‑time, low‑latency work, with optimizations for coding, sub‑agent collaboration, and computer‑use scenarios. Compared with Sonnet 4 from May, it’s well over 2× faster at about one‑third the cost, while delivering “near‑frontier” performance on many coding tasks.
Why “more economical”? Compare peers on pricing:
Sonnet 4.5: $3 / $15 (input/output).
Opus 4.1: $15 / $75 (input/output).
This means you can hand off execution steps that don’t need the “strongest brainpower” to Haiku 4.5. Bills slim down immediately and responses feel snappier. If you run many agents, hit backend services, or trigger calls frequently from your IDE, the value is obvious.
Summary: Haiku 4.5 = coding ability close to Sonnet 4 + faster speed + one‑third the price. It’s not a “make‑do small model”, it’s a “deployment‑grade workhorse”.
Highlight 2: Plan with Sonnet, execute with Haiku (the 2.0.17 default pairing)
In Claude Code 2.0.17, a crucial detail: Plan mode uses Sonnet by default for planning, then automatically switches to Haiku for execution. That mirrors real workflows in the terminal/VS Code: deep reasoning with Sonnet, batch implementation with Haiku.
Version signal: the VS Code extension is now 2.0.17, released with the October update.
Official framing: Sonnet 4.5 remains the primary engine behind Claude Code, handling long tasks and complex planning; Haiku 4.5 fills the execution seat with more speed and lower cost.
This plan/execute layering follows the familiar pattern of “frontline model for Plan, next‑tier model for implementation” — except now Haiku 4.5 takes the more capable spot on the execution side.
Real‑world take: Opus is phenomenal (and pricey); Sonnet is steady; Haiku now carries
Opus: expansive ideation, cross‑stack architecture, deep retrieval — rock‑solid. But the bill is hard to stomach. If you also hand it execution, you get great results and a racing heartbeat.
Sonnet 4.5: currently the most usable “coding brain” in the Claude family. In Plan mode it thinks clearly: strategy, steps, and risk flags are all there.
Haiku 4.5: the MVP for execution. Running agent subtasks, fanning out in parallel, scaffolding, moving files, getting tests green — it’s fast and frugal. For front‑end scaffolds, mass renames, fixture generation, it barely “breaks a sweat”.
In short: Use Sonnet for expensive “thinking”; give Haiku the bulk “hands‑on”. Your speed goes up, costs come down, and stability is easier to control.
Keep the Plan→Exec split: clarify the hard parts, then dispatch subtasks in parallel; let Haiku 4.5 handle execution.
Lean on parallelism and sub‑agents: spin up multiple Haiku 4.5 workers; coordinate with a Sonnet 4.5 orchestrator.
Make costs visible: track API usage, measure call shares across Haiku/Sonnet/Opus, and review a sprint‑length invoice.
Match model to scenario: real‑time assistants, support tickets, low‑latency orchestration → favor Haiku 4.5; complex refactors and multi‑repo surgery → Plan with Sonnet 4.5.
No flashy tricks here — this update makes “useful + economical” a daily development default.
Plan with Sonnet, execute with Haiku. Code moves fast and spending is transparent.
What’s your take? Which tasks see immediate gains with Haiku 4.5? Share real‑world results in the comments.
One‑line takeaway: AI coding speed can’t fix fuzzy specs. To make Claude Code truly low‑friction, break every “etc.” into a checklist, turn acceptance into an executable script, and let automation run the tests.
Have you ever written a requirement like:
“Show key device info on the home page (power, volume, etc.).”
Then at QA time, the AI only implements the first three. The “etc.” simply disappears.
It’s not that the AI isn’t smart; the signal you gave wasn’t precise. Claude Code “does less” when your instructions are vague—it will reasonably but incompletely narrow the scope. That’s not a bug; it’s a boundary reminder for human‑AI collaboration.
By the way, Anthropic just bumped Haiku to 4.5: cheaper, faster, and coding performance close to Sonnet (now available in Copilot too). It’s faster, yes—but it still won’t auto‑complete your “etc.”
What you see: the less you write before “=”, the less you get after it
Claude Code reads this as three explicit items + one implicit “etc.”
Result: code, automated tests, and docs all cover only the first three
It’s not AI “cutting corners.” It’s AI prioritizing what’s verifiable. Explicit items can produce code and tests; ambiguous ones get skipped. This shows up repeatedly in research and industry reports—with incomplete or vague specs, AI helpers are more likely to miss features, even when they produce code faster.
A copy‑and‑use solution: don’t let “etc.” ship to production
Convert the checklist into Gherkin scenarios, then have AI generate the automation (e.g., Cypress/Playwright):
Feature: Device key info panel
Scenario Outline: Render each key info item
Given the device exposes "<name>" with value "<value>"
When I open the home page
Then I should see "<label>" and formatted "<value>"
Examples:
| name | value | label |
| Power | On | Power |
| Volume | 70 | Volume |
| Brightness | 40 | Brightness |
| … | … | … |
Research shows: generating Gherkin from user stories first, then turning it into executable tests (Cypress, etc.) significantly improves coverage and control.
3) Put reuse in the data layer to avoid re‑implementing the same thing
Claude Code isn’t slacking. It follows the boundaries you set.
When you turn ambiguity into structure and acceptance into execution, AI becomes a reliable teammate.
Where does your team most often write “etc.”? How are you addressing it? Share in the comments.
Ever felt this pain? A device reports multiple states within a minute and your database turns into a wall of “noise”.
Here’s a general, reusable practice: in Rails, merge repeated reports that arrive within a short, configurable time window into a single “golden record”.
I’ll use Claude Code with a document‑driven approach: start from a single page of specs → generate BDD scenarios → validate with RSpec → minimal implementation → write findings back to the doc. The loop is fast, stable, and traceable. If you’re new to Claude Code, the official docs outline a clear playbook and best practices.
An “intelligent fitness band” may report multiple heart‑rate/status events within one minute. To avoid redundancy, we want to:
Within a configurable time window (default 1 minute), grouped by device and type, merge multiple events into one;
Keep the latest record within the window (i.e., the last one wins);
Write to a unified work record table, ensure idempotency, and keep merge provenance for audit and traceability.
Document‑Driven
Put the above into a single “feature spec” document. That document doubles as the product contract and acceptance criteria. From it, we derive BDD scenarios and tests. This follows the same spirit as “Specification by Example/ATDD/BDD”: examples are the single source of truth.
Claude Code + BDD: from doc to executable acceptance
It’s a terminal‑native coding copilot that actively pulls context. It connects code, docs, dependencies, and scaffolding, turning ideas into runnable structure quickly. The docs include hands‑on tips such as how to organize repositories so the agent “understands” your intent.
Prompt template (paste into Claude Code)
Role: Senior Rails engineer + test coach
Goal: Implement “device report merge” as a reusable capability
Deliverables:
Based on the spec below, generate RSpec BDD scenarios and edge cases (Rails + Capybara/request/service layer);
Provide a minimal implementation skeleton (service object, queries, transaction, idempotency strategy);
Propose rollback and audit logging;
Write all assumptions back to the doc under “Open Questions”.
Attachment: the feature spec (paste the “public version” requirement above)
Why RSpec + BDD
RSpec reads like behavior. Tests are documentation. Rails’ official guide also clarifies test layers. With Capybara/request specs you can do API acceptance and system flows.
As a data platform, within one minute, multiple reports of the same device and record type should be merged into one, taking the last record as the truth. This keeps queries clean and metrics correct.
Key scenarios
In‑window merge: t=00:10 and 00:40 → save 1 row, payload from 00:40.
Cross‑window no‑merge: t=00:10 and 01:15 → save 2 rows.
Idempotent writes: repeated requests do not produce duplicate merge results.
Auditable: keep source IDs and window parameters.
Tip: Put these examples in the doc/feature file so they serve both discussion and automated tests.
Keep the spec in the repo root with a consistent name so Claude Code can auto‑pull context.
Advance one micro‑goal at a time: get “in‑window merge” green first, then expand to idempotency and audit.
Write back to the doc frequently: new edge cases, pitfalls, and decisions go to “Open Questions/Decision Log”.
Backups and milestones: long sessions may compress context—split sessions at milestones, tag them, and keep local snapshots. AI is a power tool; you are still the craftsperson.
Industry feedback echoes this: Claude Code is rolling out across teams, long‑context models keep improving, and large‑repo analysis is getting steadier.
Document‑Driven + BDD does not mean “write more docs”; it means making the doc an executable product contract. With Claude Code, you compress the chain from idea → tests → implementation → review to hours. Collaboration gets smoother, delivery more stable, and reading the code feels like reading a story.
How would you land this merge capability in your team? Any stronger idempotency key designs? Share in the comments.
Write an absurdly short prompt and the agent patches Web (TypeScript), App (Flutter), and API (Rails) in one go: adds the feature, wires permissions, localizes strings, and even creates migrations and runs checks. The best part: easy to write, easy to reuse.
Claude Code has plugins: slash commands, sub‑agents, MCP servers, and lifecycle hooks bundled into installable units. Teams can turn cross‑stack edits into a standard pipeline.
MCP (Model Context Protocol) is like giving AI a USB‑C port: plug IDEs/agents into your repos, databases, and tools with structured read/write and execution.
Remote MCP is supported: connect to cloud services with auth; in enterprise multi‑repo/multi‑environment setups it’s much simpler.
Official best practices for agentic coding emphasize structured goals and checklist‑style requirements, making outcomes more reliable.
New models and ecosystem momentum help. Sonnet 4.5 is stronger for complex agents and “computer use” scenarios; paired with plugins/MCP it makes the path from prompt to patch much smoother.
This “clear landing points + checklisted requirements” style works best for agentic coding. Claude Code also recommends structured instructions to reduce iteration.
Let standards handle the complex parts, and humans keep the simple parts.
With one structured prompt + pluginized agent + MCP,
you can flatten the “cross‑stack, cross‑repo, cross‑language” busywork.
You focus on defining the requirement and making the final call; let the machine run the rest.
What do you think? Would this Session Auto‑Lock approach fit your project?
This methodology specifically addresses three major pain points: “AI can write code but doesn’t understand HarmonyOS,” “ArkTS keeps throwing errors,” and “regression testing can’t keep up.”
The core principle: Drive Claude Code with documentation, and secure quality through BDD automation.
Introduction: Don’t Chat Randomly with AI – Write Good Documentation First
Many developers immediately tell AI: “Help me build a HarmonyOS app for Bluetooth scanning, connecting, and time synchronization.”
The result? Claude Code produces seemingly sophisticated code that turns completely red upon compilation. Are you familiar with ArkTS? Do you understand the ArkUI lifecycle? How do you handle regression testing? All uncertain.
HarmonyOS officially provides ArkTS coding standards, ArkUI declarative development paradigms, and performance practices – these are standard components that Claude Code “crashes repeatedly if not learned properly.”
ArkTS is HarmonyOS’s primary language, sharing roots with TypeScript but having its own semantics and ecosystem. Writing with old JavaScript habits makes it easy to fall into traps.
The good news is: Claude Code already supports “reading repositories, editing files, writing and running tests, executing terminal commands.” As long as you provide the right context, it can consistently produce engineered results.
I. Documentation-Driven Programming: Make Claude Code Follow Your Lead
Objective: Clearly explain requirements, value, implementation, and acceptance criteria on one page; let Claude Code “build according to specifications” within the HarmonyOS context.
1) Write “One-Page” Requirements (can be directly placed in repository /docs/feature-xxx.md)
Functionality: Bluetooth device scanning → list display → connection → time synchronization → status reading → command sending → disconnection
Business Value: Store associates complete device initialization in 1 minute, reducing manual errors by 80%
Platform: HarmonyOS NEXT, ArkTS + ArkUI; DevEco Studio build
Interface/Model: Device names start with XX; display battery level after connection; “injection” command returns success flag
Acceptance Criteria (BDD format):
Given Application is on the home page
When Click “Start Scan”, see device starting with XX in list and click Connect
Then Enter connected page, see device name/battery level; clicking “Sync Time,” “Refresh Status,” “Send Injection Command” all return success
Remember to include key ArkTS/ArkUI constraints and best practices in documentation: naming, state management, performance considerations, etc. (from official style guides and state management guidelines). Provide specifications directly to Claude Code – it will follow these rules during generation.
2) Initial System Prompt for Claude Code (place in .claude/primer.md)
Test-first approach: Generate BDD scenarios and ArkTS test stubs first, then supplement business code
Failure retry: compilation error → read error → modify minimal set only
Officials and community both emphasize standards-driven coding, which significantly reduces rework and performance issues.
Claude Code itself also recommends customizing context and environment (e.g., ignoring irrelevant files, providing dedicated README/CONTRIBUTING), which can significantly improve hit rates.
3) Place “Process Description + Acceptance Steps” in README
The image below shows our manual acceptance steps for this feature, used for manual verification and providing context for Claude Code:
This text will be absorbed by Claude Code as “executable documentation”: it can write UI flows first, then supplement logic and tests (Claude officially claims to “write and run tests, edit files, commit code”).
II. BDD Business-Driven Testing: Transform “Documentation” into “Executable Specifications”
Then I should see a device starting with "XX" in the list
When I tap "Connect" on that device
Then I should be on Connected Page with name and battery shown
The advantage of Gherkin/BDD is transforming “business language” directly into executable specifications, continuously validating whether system behavior matches documentation.
DevEco/arkXtest System: Provides unified framework for unit testing + UI testing + performance testing; UI test scripts execute on top of unit tests, meeting end-to-end validation needs.
IDE Support: DevEco Studio integrates testing framework and code test entry points, facilitating execution on real devices/simulators.
Some documentation also mentions HarmonyOS/ArkTS testing capability upgrades in the NEXT version, supporting more modern syntax and parameterization techniques, which can be adopted in your engineering templates as needed (based on official and authoritative sources).
3) Example: UI Scenario Test Framework (extracted from actual project structure)
If not scanned, try clicking “Start Scan,” otherwise log and skip
Wait for device discovery
Locate device with specified prefix and click connect, assert entry to connected page
This type of UI test case structure aligns with arkXtest’s design philosophy: drive UI operations with APIs, consolidate results uniformly in the testing framework.
4) Make “Test Red → AI Fix → Test Green Again” a Pipeline
Claude Code Loop: When failures occur, paste compilation/runtime logs back into conversation for minimal modification fixes (Claude’s official “environment tuning + context minimization” recommendations are crucial here).
IDE/CI Integration: One-click execution in DevEco Studio locally; CI integration with real devices/simulators for regression (refer to DevEco test entry points and official testing documentation).
Visual Acceptance: Place the manual acceptance step diagrams above in wiki, run Gherkin + UI tests for each version, ensuring “documentation is truth.”
III. Implementation Manual: 7-Step Checklist from 0 to 1
Speed: With the “documentation as code” approach, Claude Code is more stable on HarmonyOS projects, reducing back-and-forth guessing.
Quality: arkXtest + DevEco testing aligns unit tests/UI/performance, automates regression, leaving manual work for exploratory validation only.
Understanding: Standards and performance practices written in documentation, humans and AI follow the same rules, new team members can master ArkTS/ArkUI basics in 1 day.
When you write documentation that can directly accept, Claude Code has the possibility to write correct ArkTS on the first try;
When you get BDD scenarios running, each version can have one-click regression.
This is how we consistently achieve “faster speed + more stable quality” in HarmonyOS projects.
What do you think this approach is still missing? Share your scenarios in the comments.
Install Codex and BMAD, map BMAD’s “multi‑role experts” into AGENTS.md, then in the terminal say “*agent analyst”. Codex will run a BMAD‑style agile loop from requirements to delivery.
Two pain points in projects: no one writes clear requirements, and even when they do, no one lands them properly.
BMAD turns roles like Business Analysis, Architecture, Product, Development, QA, and Scrum into version‑controlled Markdown agents. Combined with Codex’s AGENTS.md, you work as if an AI squad is embedded in your repo.
AGENTS.md is a “README for agents.” It lives at the repo root, as standard Markdown. Use it to document your build/test/style/PR process and any local conventions.
Codex, Cursor, Gemini, and other agents read it; agents will follow rules you define here.
To help Codex quickly locate BMAD roles, add a small “BMAD Agents” section to AGENTS.md and list a clean mapping. Many teams prefer a YAML snippet for clarity and tooling, for example:
# BMAD Agents
agents:
- id: analyst
path: .bmad-core/agents/analyst.md
- id: architect
path: .bmad-core/agents/architect.md
- id: bmad-master
path: .bmad-core/agents/bmad-master.md
- id: bmad-orchestrator
path: .bmad-core/agents/bmad-orchestrator.md
- id: dev
path: .bmad-core/agents/dev.md
- id: pm
path: .bmad-core/agents/pm.md
- id: po
path: .bmad-core/agents/po.md
- id: qa
path: .bmad-core/agents/qa.md
- id: sm
path: .bmad-core/agents/sm.md
- id: ux-expert
path: .bmad-core/agents/ux-expert.md
Note: AGENTS.md is Markdown and doesn’t require YAML—tables or lists are fine. The key is to let agents reliably locate those role .md files.
7. *perform-market-research — Create market research doc
8. *research-prompt {topic} — Generate deep research prompt
9. *yolo — Toggle skip confirmations
10. *exit — Exit Business Analyst persona
BMAD’s Web/IDE flows surface *help and *analyst as common entry points. Bringing the same “numbered options + task‑driven” style into Codex gives you a similar experience.
Reminder: BMAD’s Analyst/Architect/PM/Dev roles each define commands/checklists in agents/*.md. Some tasks (e.g., brainstorming) reference files in .bmad-core/tasks/*. If paths are correct, the agent can load them.
Codex naturally runs commands and reviews code in your repo (and can be @‑mentioned in PRs). When paired with AGENTS.md rules for tests/style/build, you get a tight loop of local validation and automated review.
BMAD turns human roles into versioned code. Codex turns execution into automatable actions. Wire them together and you get an AI team that doesn’t tire, reproduces well, and never forgets the rules.
Which project will you hand to this squad first? Share in the comments.
This article demonstrates how to quickly develop a Bluetooth device management app for Huawei’s HarmonyOS 6 using Claude Code. It covers typical features such as receiving Bluetooth advertisements, connecting to devices, setting the time, synchronizing status, performing infusions, and retrieving logs. The article also focuses on strategies for fixing ArkTS compilation errors and implementing TDD/BDD-oriented testing.
— Suitable for teams aiming to build BLE device applications through a “write, test, iterate quickly” approach.
Quickly locate BLE-related entry points, cross-references, and dependencies using commands; maintain consistency with batch renaming and interface extraction.
Assisted Fixing of ArkTS Compilation Warnings/Errors:
Automatically points out rule violations like arkts-no-spread and unknown/any, suggesting replacement code and type completion.
Generating Test Skeletons and Adding Assertions:
Quickly create and extend unit tests and UI integration tests based on Hypium.
By combining these capabilities, a high-frequency feedback loop of “modify → run tests → analyze logs → fix/refactor” can be established, significantly shortening the cycle from protocol design to an end-to-end working application.
3. Detailed Breakdown of the Bluetooth Interaction Flow
Covers major AD types like Flags, Complete/Incomplete UUIDs, and Manufacturer Specific Data, making it easy to quickly identify the target device.
Associated Discovery Service: entry/src/main/ets/utils/BleDiscoveryService.ets encapsulates the discovery list, sorting by RSSI, automatic stopping on timeout, and a “trusted device” caching strategy. The UI side can directly subscribe to status/result callbacks.
2. Connecting to a Device and Enabling Notifications
Synchronize Time: entry/src/main/ets/utils/BleService.ets, writes 0xA7 containing year, month, day, week, hour, minute, and second, then verifies through the notification channel upon completion.
Read Status: entry/src/main/ets/utils/BleService.ets → Wait for notification → Parse: entry/src/main/ets/utils/BleResponseParser.ets
Parses battery status, voltage/charge, remaining dose, single/daily limits, alarm bits, today’s usage, maximum log entries, etc.
entry/src/main/ets/utils/BleCommand.ets: The dose is encoded as a 4-byte little-endian integer with a precision of 1/1000, accommodating “large/high-precision dose” scenarios.
Recommendations:
Perform pre-validation against the single/daily limits from the status, and split into packets or send in multiple parts if necessary.
Pay attention to MTU (typically 20 bytes) limitations and command length design.
The “ path calls record parsing: entry/src/main/ets/utils/BleResponseParser.etsparseRecord(...)
Supports two types of structured data: injection records and operation records.
If you need to “fetch historical records by index/type,” you can add a READ_RECORD_WITH_TYPE constructor in BleCommand (a template is commented out in the project), which seamlessly integrates with the parsing logic above.
For example, if connectionPromiseResolvers is not declared, it needs to be added as Map<string, () => void> for external resolution of connection Promises.
arkts-no-spread:
Object spreading is forbidden when writing characteristic values. Directly modify existing.characteristicValue = ... (see the BleService.ets section).
unknown/any and Loose Literals:
In notification callbacks, remove temporary Record<string, string>/unknown assertions and use strong typing instead.
Notification Matching and Logging:
Use lowercase UUID comparison and detailed logging of service/char UUID and hexadecimal payloads for easier on-site debugging.
These fixes, without changing business semantics, make the code satisfy both ArkTS semantics and Lint rules, resulting in a stable and maintainable build.
Most BLE steps are asynchronous callbacks: set reasonable timeouts for scanning/connecting/writing/notifications (e.g., 5–15s) to avoid false positives.
For critical UI text and buttons, try finding them multiple times to improve stability.
Use hexadecimal logs as an assertion aid (see BleService.ets/BleService.ets).
The sequence “subscribe → enable notifications → read once” can significantly improve the success rate of the first downstream response for some devices.
MTU and Command Length:
A typical BLE 4.2 MTU is only 20 bytes. Complex commands need to be split or the negotiation strategy on the device/firmware side needs to be upgraded.
Little-Endian Encoding and CRC:
The total command length, field encoding, and CRC16 (CCITT/False) calculation must be strictly aligned with the firmware.
Use HiLog Instead of console:
Use a unified domain/tag and %{public} output (see docs/hilog-migration-summary.md) for easier offline and on-site troubleshooting.
Permissions and Bluetooth Status:
Unified entry point: entry/src/main/ets/utils/BlePermissionManager.etsensureBleReady(...). Handle permissions and adapter status all at once before starting a scan.
Thanks to Claude Code’s “code navigation + compilation repair + test generation” features, we completed the entire cycle from protocol design to an end-to-end working solution with minimal back-and-forth:
Bluetooth Interaction: The full chain of scanning, connecting, notifications, commands, and record parsing is connected.
ArkTS Fixes: Strictly follows semantics and Lint rules for a stable build.
TDD/BDD: Unit and UI integration tests cover major scenarios, facilitating continuous regression.
Recommended Next Steps:
Add the READ_RECORD_WITH_TYPE command constructor in BleCommand to integrate with the existing parser and complete the “fetch logs by index” feature.
Introduce more granular ViewModel layer state management and data persistence to create a complete “cloud-device-edge” loop with record synchronization.
Refine exception codes and retry strategies to enhance robustness during network disconnection/reconnection.
The HarmonyOS BLE API version used in my previous project (v14 and earlier) had limitations such as single-scan restrictions and incomplete resource management.
Now, using Claude Code to generate and modify code, I was able to understand and automatically migrate to the latest HarmonyOS BLE API v15+, enabling multi-scan and more robust connection management.
Finally, I documented the complete migration process from v14 to v15+, focusing on multi-scan support, advertising data parsing, and connection management optimization.
In the HarmonyOS ecosystem, BLE (Bluetooth Low Energy) is a crucial technology for interconnecting smart devices. As application demands have grown, the limitation of single-scan has become increasingly apparent, especially in scenarios requiring simultaneous scanning for multiple devices or services.
HarmonyOS introduced multi-scan support in API version 15, allowing applications to create multiple independent scanning instances, thereby improving resource management and scanning flexibility.
However, the existing project code was still based on the old API, leading to the following issues:
Single-Scan Limitation: Only one scanning task could be performed at a time, failing to meet the demand for multi-device scanning.
Incomplete Resource Management: Scanner instances were not managed independently, causing state confusion.
Lack of Advertising Data Parsing: Scan results did not parse advertising data, affecting device information retrieval.
Simple Connection Management: The absence of retry and automatic reconnection mechanisms compromised connection stability.
Before starting the migration, the first step was to download the official documentation to let Claude Code understand the new features and changes in HarmonyOS BLE API v15+.
2. Use Claude Code to Generate Migration Suggestions
I provided the official documentation content to Claude Code and asked it to analyze the problems in the existing code and execute the steps to migrate to the latest API.
Terminal window
> @harmony-os-docs/cn/doc/harmonyos-guides/ble-development-guide.md Recommend using the scanning method supported from API version
Based on the analysis, Claude Code generated new `BleService.ets` code, including support for multi-scanning, advertising data parsing, and enhanced connection management.
I conducted a detailed review of the generated code to ensure it met project requirements and performed comprehensive testing on actual devices to verify the stability of the multi-scan and connection management features.
Finally, I had Claude Code generate a complete summary document, recording the entire process of migrating from HarmonyOS BLE API v14 to v15+, including multi-scan support, advertising data parsing, and enhanced connection management features.
The API v15+ upgrade brings significant improvements:
✅ Supports concurrent multi-scanning
✅ Independent resource management
✅ Better error isolation
✅ Flexible configuration options
✅ Enhanced stability
By following this guide, you can smoothly migrate from v14 to v15+, taking full advantage of the new API to provide a more stable and efficient BLE connection experience.
Turn yesterday’s group and private chats into a one‑click daily report: top keywords, key people, to‑dos, and risk alerts — fully automated.
This guide walks you through using Claude Code + chatlog to squeeze insights from chat data, with copy‑paste‑ready prompts.
chatlog is an open‑source tool that supports local WeChat database access, automatic decryption, HTTP API, Webhook, and MCP Streamable HTTP, so Claude Code can use it directly as a “data tool”.
Treat chats as data and use Claude Code to refine them into an actionable team daily.
Spend 3 minutes skimming each day and get time back for what actually matters.
Using Claude Code to handle Git branch merges and resolve conflicts
In modern software development, Git merges and conflict resolution are everyday tasks. Claude Code, as an intelligent development assistant, can significantly speed up and harden these workflows. This post walks through practical scenarios showing how to use Claude Code to handle a variety of merge cases.
You’re building a React app. The feature/user-auth branch implements user authentication and needs to be merged into main. Both branches modified App.tsx.
In a Node.js microservice, feature/database-migration updates the DB schema while main adds new API endpoints. Conflicts span multiple files and dependencies.
There’s a production critical bug. hotfix/payment-calculation contains a fix, but conflicts with develop. It needs to be merged into both main and develop.
Claude Code turns Git merging from a tedious manual process into a smarter, automated workflow. With semantic understanding, automated testing, and intelligent conflict resolution, it improves both developer efficiency and code quality.
Key advantages:
Up to 80% less time spent on conflicts
Automated tests guard merge quality
Smart suggestions avoid common mistakes
Complete, reviewable operation history
In production projects, integrate Claude Code into CI/CD for even more automation. While Claude Code is powerful, architectural decisions should still be reviewed by humans.
By default it’s app-release.apk.
When sending to QA or uploading to CI artifacts, everything has the same name.
It’s guesswork to identify versions — frustrating and error-prone.
Any Android Gradle Plugin (AGP) version works, but naming approaches differ.
The robust approach is to copy/rename after assemble. That’s the path used below
and is widely adopted under newer AGP APIs.
Flutter’s --dart-define values are passed via Gradle -Pdart-defines as Base64.
We can decode and read them in build.gradle.
Copy and use: add this to android/app/build.gradle
Place under the android { ... } section with applicationVariants (Groovy syntax).
Idea: get branch name + decode --dart-define + build the new file name + copy/rename after assemble.
This “copy and rename” technique is more reliable across AGP versions.
Bake the naming rule into the build step so every artifact is self‑describing.
QA hand‑off, tracebacks, and bug hunts all get easier.
Claude Code turns these “small but annoying” chores into a one‑time setup you can reuse.
Anything else you want included in the name? For example, short git hash, channel, target ABI?
Use Claude Code as your “pair programmer who knows HarmonyOS ArkTS” to build a BLE manager that can scan / connect / read-write / subscribe — runnable on a real device with logs for debugging.
Anyone who has built a BLE manager knows: the protocol is fragmented, device vendors differ wildly, and versions change fast. HarmonyOS/ArkTS docs are large and spread out — newcomers get lost easily.
My practical approach: feed the official documentation into Claude Code and let it be a “code-with-the-manual-open” partner in the editor; we stay focused on business logic and debugging flow.
ArkTS is the preferred native language for HarmonyOS, enhancing TypeScript with stronger static checks and performance (see official docs: https://developer.huawei.com/consumer/cn/arkts/).
BLE on HarmonyOS uses @ohos.bluetooth.ble, with core capabilities for scanning, advertising, and GATT client/server.
Real data interaction goes via GATT: reading/writing characteristics and subscribing to notifications.
The benefit of this combo: fewer guesses and directly coding to the docs. Claude Code searches, cross-checks, and completes; you control architecture, state machine, and logging.
Preparation | Put the docs into Claude’s “stomach”
Goal context
We’ll build an ArkTS app that connects to BLE devices.
Download official docs and add a local folder
Grab the ArkTS & BLE docs (guides + API references) from Huawei Developers, and store them offline in your project under /harmony-os-docs/.
Write a CLAUDE.md (sample)
Treat Claude Code as a teammate and require it to strictly consult these docs before generating ArkTS code and explanations:
# CLAUDE.md (excerpt)
You are my ArkTS + BLE copilot. Always quote and align with these docs:
- If API differs by version, note the min API level & capability.
The key here is to constrain Claude: if it’s unsure, check; if it’s uncertain, annotate. ArkTS style guidance and performance tips can be referenced directly in code review.
ohos.permission.LOCATION (location permission often required for scanning; follow UX requirements for foreground disclosure)
Module entry
ArkTS BLE uses @ohos.bluetooth.ble. Scan results come from ble.on('BLEDeviceFind', cb). After discovering a device, create GattClientDevice to connect and perform GATT read/write/subscribe.
The official “BLE best practices for scanning/connection state management” also provides suggestions for a business-layer state machine; consider persisting those.
Use the BLEDeviceFind event for scan results; remember to off when stopping.
Connect via createGattClientDevice(deviceId) + connect(), which matches official usage.
Interactions rely on GATT: read/write/subscribe (notifications). Method names/params vary by version; always defer to the API reference. The overall approach remains: identify service/characteristic → read/write/subscribe.
Unified error callbacks and key logs make packet capture and production debugging easier. Best practices emphasize “reasonable Bluetooth usage” and foreground/background policies.
Tip: If your peripheral uses a custom GATT profile, align the Service/Characteristic UUIDs and payload protocol up front — it makes the client side much smoother.
Make it read docs first: include @harmony-os-docs/... paths in your prompt and ask for citations.
Ask for a “design doc” up front: folder layout + state machine + event flow + key API mapping table.
Call out version differences: require an “API version matrix”, e.g., startBLEScan vs startScan, and mark “verify on local SDK” where uncertain.
Auto-generate tests/stubs: request quick cases with mocked callbacks/notifications, covering reconnect, permission denial, etc.
Log protocol: agree on a [BLE] prefix + key fields for easy collection; follow “reasonable Bluetooth usage” best practices for scan window and frequency.
In short: feed Claude the docs and tell it to write ArkTS code by the book. You own the boundaries, state, and debugging. In an afternoon, you can have a runnable BLE manager. Thoughts? Share your device model and pitfalls in the comments — I can help with parameters and debugging strategies.
Anthropic has emerged as a prominent player in artificial intelligence. Its core vision is to build AI systems that are trustworthy, interpretable, and controllable—and to lead a “safety‑first race.” Unlike many peers, Anthropic’s story did not begin with a single technical breakthrough but with a deep philosophical and governance split. From day one, it has treated AI safety and ethics as a foundational mission, embedding them into the company’s legal structure, technical methodology, and commercial strategy.
Anthropic was founded in 2021 by a group of former OpenAI employees, led by siblings Dario Amodei (former VP of Research at OpenAI) and Daniela Amodei (former VP of Safety and Policy). In late 2020, they left OpenAI with five colleagues due to fundamental disagreements over the company’s direction.
Dario and others were concerned about OpenAI’s 2019 transition from a non‑profit to a “capped‑profit” entity and its deep partnership with Microsoft. They believed this shift moved the mission from “benefiting all of humanity” toward commercialization and profit, relegating AI safety and ethics to a secondary role. As Dario put it, they “could foresee the exponential growth of artificial intelligence,” and AI companies urgently needed “a set of values to constrain these powerful programs.” The founding of Anthropic was a direct counter to what they saw as sacrificing safety for commercial priorities.
Public benefit corporation: writing the mission into the charter
Anthropic is a Delaware public benefit corporation (PBC). This legal structure requires the company to balance shareholder interests with a public‑benefit purpose. For Anthropic, that purpose is to develop AI systems that are reliable, interpretable, and controllable.
To prevent commercial pressure from eroding its safety mission, Anthropic established the Long‑Term Benefit Trust, which holds a special T‑class of shares and can elect a majority of the board. This governance design aims to legally insulate the safety mission—at least in part—from purely financial interests, addressing the limitations of traditional corporate governance in confronting potential catastrophic risks from AGI.
From the founders’ departures to the company’s structural design, Anthropic’s core narrative is clear: rapid AI progress requires a mission‑driven governance paradigm. This not only builds a “responsible” brand but also offers a credible choice for those concerned about AGI’s commercialization risks.
Constitutional AI (CAI) aims to train AI to be “helpful, honest, and harmless.” Its core idea is to introduce a human‑written “constitution”—a set of principles that guide the model to self‑critique and revise its outputs during training. Unlike the common approach of deflecting or blurring sensitive topics, CAI enables more direct responses under principled constraints and requires the model to explain refusals when appropriate.
Training occurs in two stages. In supervised fine‑tuning, the model critiques and corrects its own problematic answers using constitutional principles, producing a “less harmful” dataset for further tuning.
Reinforcement learning from AI feedback (RLAIF): an engine for scalable alignment
The second stage uses reinforcement learning. Anthropic applies RLAIF: the model generates two candidate answers, then evaluates and selects a preferred one according to a randomly chosen constitutional principle; these judgments train a preference model so the final policy better adheres to the constitution.
Compared with the more common RLHF, Anthropic argues RLAIF is more efficient and scalable (less dependent on human labeling), improves interpretability and transparency by expressing goals in natural language, and somewhat reduces human bias. Critics note the constitution is still human‑written and thus subjective, and that reducing human intervention raises questions about accountability and oversight. The tension between “automation” and “human control” is central to understanding Anthropic’s approach.
Since launching Claude 1 in March 2023, Anthropic has iterated rapidly on a roughly 4–8 month cadence: Claude 2 (July 2023), the Claude 3 family (March 2024), and beyond—demonstrating that Anthropic is both a research organization and a product company competing at the frontier.
Each generation pushed forward on context length, reasoning, and multimodality: from Claude 2’s 100K‑token context window to Claude 3’s 200K, then toward ~1M tokens in extended reasoning modes with Claude 4. Starting with Claude 3, image+text is supported; coding, math, and logical reasoning improved markedly.
Artifacts: introduced with Claude 3.5 Sonnet; generates and previews code, SVGs, websites, and more in a dedicated pane.
Computer Use: debuted in 3.5 Sonnet beta; the AI can operate a virtual desktop, simulating cursor, clicks, and input across applications for multi‑step tasks.
Hybrid Dual‑Mode Reasoning: added in Claude 3.7 and Claude 4; dynamically switches between fast responses and “extended thinking,” invoking tools (retrieval, code execution) when needed.
These capabilities move AI from assistantship toward autonomous agency, while bringing AGI‑related risks closer to the real world.
Table 1: Release timeline and core capabilities of Claude
Anthropic’s valuation exceeded $61B by late 2024 and reached roughly $183B after its Series F in mid‑2025. The latest $13B round led by ICONIQ further strengthens its position across enterprise, developer, and premium consumer markets.
Revenue has grown rapidly as well: annualized run‑rate rose from about $1B in early 2025 to over $5B by August. Claude Code alone reached more than a $0.5B annualized run‑rate within three months, showing clear conversion from technology to competitive advantage.
Anthropic forged deep partnerships with Amazon and Google: it designates AWS as a primary cloud provider and offers models on Amazon Bedrock; Google has also committed billions in investment and cloud infrastructure support. Access to compute such as Trainium and TPUs enables large‑scale frontier training.
In 2025, an internal memo by CEO Dario Amodei was leaked, candidly acknowledging that to remain competitive the company had considered seeking investment from Middle Eastern sovereign capital—and recognizing the “moral risk.” He wrote: “I really wish we didn’t have to be in this position, but we are,” and, “‘No bad actor should benefit from our success’ is a hard principle on which to run a business.”
This highlights Anthropic’s core contradiction: in a capital‑intensive race, even governance with public‑benefit features cannot fully escape the pressures of “racing to the bottom.”
Table 2: Anthropic’s major financing rounds and valuation
Dario Amodei has expressed an aggressive timeline for AGI, even suggesting a “singularity” could arrive as early as 2026. This urgency helps explain Anthropic’s immense capital needs and the difficult trade‑offs it faces in competition.
Beyond setting a technical baseline with CAI, Anthropic actively participates in US AI policy discussions. Its collaboration with Palantir brought Claude 3/3.5 into “classified environments” for the first time—both validating capabilities and safety, and touching one of the model’s most controversial application domains.
Products like Claude Code make modern, automated software engineering a practical reality and will drive change across healthcare, education, and finance. Challenges remain: how to ensure systems apply ethical principles robustly in complex contexts; how to build regulatory and accountability frameworks; and how to rebuild public trust.
Anthropic began with a mission‑driven split and embedded safety into its corporate DNA through a PBC and trust‑based governance. Its rapid iteration and commercial growth suggest that a “safety commitment” can be a powerful market differentiator. Yet the leaked memo reveals persistent tension between capital and ideals.
Building powerful AI and ensuring its safety are not parallel lines but two sides of the same coin. As Anthropic moves forward along this complicated path, it will shape the technical landscape of AI and force us to confront a deeper question: in an AI‑driven world, how much principle are we willing to trade for power?
Claude Code is more mature for engineering workflows (subagents, slash commands, hooks). Codex (GPT‑5 High) is faster, sticks closer to instructions, and gets things done with fewer words. Use both for maximum throughput.
The community has been buzzing again about Codex CLI and Claude Code. Someone built a small tool using Cloudflare Workers + Telegram: it watches Codex GitHub releases and pushes new versions to Telegram. Both agents “worked end‑to‑end”, but the experience differed in meaningful ways.
Here’s a practical, ready‑to‑apply comparison to help you choose quickly.
Engineering/Extensibility: Claude Code wins. Subagents, custom slash commands, and hooks are very mature.
Execution rhythm/plan view: Codex shows more detail in its plan panel; Claude Code defaults to “current task only”, which makes flow feel smoother.
Instruction adherence: Codex (GPT‑5 High) followed explicit constraints (like directory layout) more strictly; in one run it emitted multi‑file /src output, while Opus bundled code into a single worker.js.
Data fetching strategy: Different roads to Rome — Codex used Atom feeds; Claude Code used the GitHub API. Both got the job done.
Observability: Codex provides /status for token use and Ctrl+T to explore conversation history (community is adding export/persistence tooling).
Speed and cost perception: In a single task sample, GPT‑5 used ~90k tokens vs Opus ~50k. Because GPT‑5 is cheaper per token, total cost isn’t necessarily higher. Treat this as an anecdote, not a benchmark.
Docs ecosystem: Claude Code docs feel more systematic; Codex docs are improving quickly.
OpenAI is positioning Codex as a full‑field development agent: CLI, IDE extensions, cloud, and GitHub integration in one. It shares state through your ChatGPT account so a task can bounce between local and cloud. The VS Code extension is live — no API key setup, plus cloud sandbox for execution.
The release cadence is fast, so media and community reviews are everywhere.
Head‑to‑Head: Findings from a Cloudflare Worker Field Test
Subagents: Split roles and context with separate system prompts, tools, and isolated memory. Great for “data gathering / solution review / scaffold generation” assembly lines.
Slash commands: Expose MCP‑discovered commands and build a team‑level action library inside your project.
Hooks: Force lifecycle actions (session start, before/after tool exec) to run every time. Think “lint → test → commit” guaranteed, not subject to model mood.
Bottom line: If you need scale and control, Claude Code’s orchestration is a clear advantage.
2) Execution Experience: Codex Is Snappier; Claude Code Flows Nicely
This isn’t a “replacement” story. Codex is a sprinter; Claude Code is a triathlete. Use the right one for the job and you’ll find they complement each other perfectly.
Which style do you prefer? Tell us — we’ll keep this comparison updated as a living “selection guide”.
TL;DR:
For personal accounts (Free/Pro/Max), go to claude.ai → Settings → Data & Privacy and turn off “Use my chats/coding sessions to improve the model.” For organizations/API, usage is not used for training by default; for extra assurance, use Zero Data Retention (ZDR) with an org API key for Claude Code. On your machine, disable Statsig telemetry/Sentry error reporting, don’t use /bug to upload sessions, and periodically clear local caches.
Anthropic is updating data policy for consumer products (including Claude, Pro, Max, and Claude Code when signed in with those accounts): you can choose whether chats and coding sessions are used for training; if you choose Allow, the retention period can be up to 5 years. If you choose Do not allow, the service side retains up to 30 days for operations and compliance. This choice only applies to newly created or “resumed” sessions (no retroactive scanning of dormant history). You can change it anytime in Privacy Settings.
For industry coverage and reminders about timing, see summaries from The Verge / Tom’s Guide.
Goal: prevent your chats and code from being used for training.
How to do it
Open claude.ai → Settings → Data & Privacy.
Turn off “Use my chats/coding sessions to improve Claude” (or similar wording).
Remember: this only applies to new/resumed sessions; deleted conversations will not be used for training. You can change this later, but data already used for training cannot be undone.
Retention rules (when you disable training): backend storage for up to 30 days.
Default is safer: Anthropic’s commercial products (Claude for Work, Anthropic API, Claude Gov, etc.) do not use your inputs/outputs for training by default.
Even safer (recommended): sign Zero Data Retention (ZDR) and use your organization’s API key in Claude Code. ZDR currently applies only to the Anthropic API and products that use your org API key (including Claude Code).
Default retention: on the API side, 30‑day auto‑deletion (if ZDR isn’t in place and absent additional legal/risk requirements).
③ Go via cloud providers (Bedrock / Vertex) for another layer of isolation
AWS Bedrock: explicitly does not use your inputs/outputs to train models; contract terms also constrain third‑party models (including Anthropic).
Google Vertex AI: has dedicated data governance and caching docs; enterprises can configure to meet compliance and minimize retention (read caching/governance details carefully).
This “cloud‑side isolation” suits highly regulated sectors, keeping orchestration, logs, and KMS within your cloud account under unified governance.
The official recommendation is to use /bug to submit issues; it bundles context for Anthropic to diagnose. Therefore avoid it when dealing with sensitive repos/code.
The community has collected an env var to disable /bug (DISABLE_BUG_COMMAND=1). This is “unofficial / may change” — for reference only.
Per official notes, the client can cache sessions locally for up to 30 days to aid resuming (configurable). In sensitive environments, regularly clean ~/.claude/ and set cache duration to a minimum.
The community has previously reported issues about .claude.json retaining conversation fragments — these are behavioral reports, not official guarantees. Add .claude/ to your security baseline and backup exclusion lists.
Myth 1: “If I delete historical chats, they won’t be used for training.”
→ After deletion, they won’t be used for training, but whether they were trained on depends on your setting at the time; training is irreversible.
Myth 2: “Using Claude Code equals enterprise‑grade protection.”
→ It depends on the account and key you use. Claude Code signed in with a personal account is still under consumer terms.
Myth 3: “If I go via Bedrock/Vertex, I’m done.”
→ You still need to configure your own permission boundaries, key management, logging policies, and cache governance. The docs are explicit.
Data privacy has never been a simple on/off switch. It’s a set of habits: right configuration + right tools + team discipline. Work through the checklist above and your code and conversations will not be shared with Anthropic for training, while you also preserve your team’s compliance boundary.
A fast series of 1.0.90–1.0.97 updates lands several practical fixes and upgrades: permission rule validation, editable memory files, Vertex global endpoints, proxy compatibility, hot‑reloaded settings, and a task list. The execution loop feels much tighter for engineers.
Over the past two weeks, Claude Code shipped multiple minor versions. They look small, but each one targets high‑frequency operations: no more fragile permission rules, edit memory files directly, route Vertex via a global endpoint for higher availability, paste images easily on Windows, and see settings take effect immediately. Below is a hands‑on rundown of the most impactful changes, with copy‑ready usage.
/doctor can surface wildcard correctness, priority overlaps, and more. This is essential for a shared, unified “allowed commands” policy across teams.
Security note: the community has reported cases where deny rules didn’t take effect or command chains found bypasses. Don’t over‑grant at the start—prefer least privilege and open up gradually.
/memory now does more than show loaded CLAUDE.md files—it lets you open and edit them directly (including multi‑level imports via @path/to/file). Benefits:
One change, shared impact: Put conventions in the repo so the whole team benefits.
Modular structure: Use @imports to manage layers for architecture, Git rules, scaffolding commands, etc.
Rapid iteration: Works great with hot‑reloaded settings.
If you’re billed via Google Vertex AI, you can now use the global endpoint to route to available regions dynamically, improving availability and stability. Great for overnight long jobs or “must‑pass” CI before submission. Docs confirm Claude Code supports both global and regional endpoints for Vertex.
Tip: available models and aliases can vary by region. When switching, double‑check the endpoint ↔ model mapping.
04 Settings hot‑reload, with a hooks caveat (1.0.90)
From 1.0.90, most settings apply immediately—no session restart needed. This tightens the tweak → retry loop.
Exception: Hooks (pre/post execution) are not replaced immediately. The current session uses a startup snapshot; after editing hooks, open /hooks to review & apply. Don’t miss this detail.
NO_PROXY support: In complex intranet / multi‑hop proxy setups, directly connect to certain hosts or subnets and bypass the global proxy. Used with HTTP_PROXY/HTTPS_PROXY, this fits CI and bastion/egress scenarios well.
Windows Alt + V: Paste images directly in the terminal, ideal for embedding error screenshots/tables into context to speed up “locate → fix”.
06 SDK callbacks & custom tools: wire in your pipeline (1.0.94)
The TypeScript SDK supports an in‑process MCP server and custom tools, so “build, lint, pre‑publish checks, canary rollback,” etc. can become tools Claude calls. Combine with callbacks/hooks to stitch together success/failure handling and audit logs.
Switch your environment to a global endpoint as per GCP docs and available models.
Confirm exact endpoints and regional model support with the official documentation.
Want Claude Code to feel snappy, steady, and fast?
Here’s a paste-and-go cheat sheet for claude config plus practical templates your team can adopt immediately.
This is a hands-on playbook: copy commands as-is, drop in configs, troubleshoot quickly. Sources are noted so you can share confidently with teammates and IT.
“Why didn’t my change take effect?”
Check precedence. Enterprise > CLI arg > project local > project shared > user. Higher levels can override.
“I want to disable all telemetry/reporting/auto-update at once.”
Use CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 to cover all four switches.
“Where do I put the settings file?”
User: ~/.claude/settings.json; Project: .claude/settings(.local).json; Enterprise: system-level managed-settings.json (see platform paths above).
Decide who wins (precedence & policy) → Decide what’s allowed (permissions allow/deny) → Decide how it behaves (model, output, notifications, env).
With the right setup, Claude Code becomes your team’s AI dev muscle memory: steadier, faster, safer. One set of templates, tailored per project, without friction.
First, use Claude Code to quickly “get a prototype working” to refine the requirements; then, return to engineering design and implementation to ensure quality and stability. Efficiency and quality, we want both.
The hardest part of product development isn’t writing code.
It’s clearly defining “what needs to be built.”
That’s why I recommend this two-stage approach:
Build a prototype in the first version, and handle the engineering seriously in the second.
Claude Code is well-suited for this strategy: it works alongside you in the terminal, handling the grunt work first, allowing you to refine it later.
Method: Use throwaway code, letting the AI take the lead to quickly explore feasible paths (similar to “Spike/Throwaway Prototyping”).
Why this approach?
Because many ambiguities are only exposed when the code is running. Screenshots, GIFs, and clickable prototypes are far more useful than a ten-page requirements document.
Claude Code’s advantages in this stage:
Directly read, modify, and run code within your local project—conversation becomes action.
Frequent actions can be turned into Slash Commands for one-click reuse, like “start backend + mock data + run locally.”
A word of caution: If you’re using a personal subscription, Anthropic recently introduced an opt-in mechanism for data training that includes Claude Code sessions. Check your privacy settings before prototyping company projects.
Trigger:Requirements are mostly finalized, and the scope is stable.
Deliverables:Database schema, API contracts, state management, module boundaries, testing strategy, etc.
Pace:Human-led, AI-assisted. You set the design anchors, and the AI builds according to the plan.
How to use Claude Code in this stage:
Let it scaffold the project, add boilerplate, and write tests based on your design.
@claude in PRs/Issues to automatically create PRs and fix minor issues (via GitHub Actions integration).
Write engineering conventions into a CLAUDE.md file or Slash Commands to ensure the AI follows project rules.
Security/Non-Functional Advice: Treat security, performance, and maintainability as “Technical Enablers” and progressively incorporate them into your Definition of Done (DoD) during the second stage. Conduct Spikes for validation when necessary.
The requirements have already been validated (e.g., redoing an old feature, a clear competitor to model after).
The primary risk is in the engineering implementation, not the product definition.
In this case, you can proceed directly to Stage 2: Design and Implementation, with Claude Code acting as a scaffolding and execution tool.
Field Guide: My Recommended Uses in Daily Development
Prototype drags on for too long: If requirements aren’t settled in 1-2 weeks, break it down into smaller stories; conduct a Spike summary if needed.
Prototype code accidentally shipped to production: Use branch isolation, directory prefixes like /proto-*, and CI checks to block it.
Unbounded second stage: Write an Engineering DoD (including security and performance), list non-functional items as Technical Enablers, and complete them sprint by sprint.
Privacy compliance: Prioritize enterprise/API channels for company projects; for personal subscriptions, always check data training opt-in settings.
Prototyping isn’t about being lazy; it’s about accelerating clarity.Engineering isn’t about being slow; it’s about reducing long-term costs.
With Claude Code, we can put these two activities in their proper place:
AI leads the prototyping, humans lead the engineering.
Take that hesitant requirement you have right now, and just get it running.
What do you think? Does your team get stuck more in the prototyping or engineering phase? Let’s discuss in the comments.
TL;DR
Want to quickly switch between different providers for Claude Code and Codex — DeepSeek / Qwen Coder / GLM / Kimi K2 / packycode, etc.? CC‑Switch is an ultra‑light desktop app built on Tauri 2.0 (installer ~5 MB). It supports one‑click switching, import current config as default, switching back to official sign‑in, and automatic backups. The new v3.1.0 adds Codex provider management and one‑click switching.
Introduction: Why do you need a “one‑click provider switcher”?
As people who write code every day, we’re often more like dispatchers of cost and speed.
Today you use Qwen Coder for speed; tomorrow you switch to DeepSeek v3.1 for complex reasoning; when compatibility issues show up, you switch back to Claude official sign‑in. If you still have to manually edit configs under ~/.claude and ~/.codex every time, it’s annoying and error‑prone.
CC‑Switch turns this into a single toggle:
Click once, and the provider switches.
Config copies are auto‑managed, rollback is safe, and you can return to official sign‑in at any time.
Truly lightweight: Built on Tauri 2.0, launches instantly. The official guidance emphasizes Tauri’s small‑footprint approach (more restrained than Electron).
In v3.0.0, CC‑Switch migrated from Electron to Tauri 2.0. In author tests the bundle size dropped from ~80 MB to ~12 MB and startup sped up 10×.
Supports both Claude Code and Codex:
Claude Code primary config: ~/.claude/settings.json (or legacy claude.json)
The official docs confirm ~/.claude/settings.json is the standard entry point.
Codex primary config: ~/.codex/auth.json (required) + ~/.codex/config.toml (may be empty)
The Codex README also states ~/.codex/config.toml is the config center, supporting MCP, etc.
One‑click switch & automatic backups:
Switching overlays the selected provider copy onto the primary config. If there’s a provider currently “in use”, CC‑Switch first writes the primary config back into that provider’s copy to facilitate rollback. Internal config v1→v2 migrations are timestamp‑backed up under ~/.cc-switch/.
Presets ready to use: DeepSeek v3.1, Qwen Coder, GLM, Kimi K2, packycode… just paste a key to configure with one click.
Switch back to official sign‑in anytime:
Prefer Claude or Codex’s official sign‑in? Add the Official Sign‑In preset. After switching, restart your terminal to use /login (Claude Code) or “Sign in with ChatGPT” (Codex side).
Privacy‑friendly:
Everything is stored locally only in ~/.cc-switch/config.json.
Windows: Download CC-Switch-Setup.msi or Windows-Portable.zip from Releases.
macOS: Download CC-Switch-macOS.zip. On first launch you may see an “unidentified developer” warning; go to System Settings → Privacy & Security → “Open Anyway”.
Linux: Download the .deb package.
Heads‑up: The author doesn’t have an Apple Developer account, so the first launch may be blocked; follow the steps above to allow it.
Add a provider
Click “Add Provider”, pick a preset or define your own, and enter the API Key.
(When using an API key with Codex, OPENAI_API_KEY is common. If you’re using official account sign‑in, switch to “Codex Official Sign‑In”.)
One‑click switch
Check the provider you want in the list, then click the radio button. CC‑Switch overlays the copy onto the primary config:
Codex: overlays ~/.codex/auth.json and ~/.codex/config.toml (creates an empty config.toml if missing).
Claude Code: overlays ~/.claude/settings.json (or legacy claude.json).
Restart or open a new terminal
Open a new terminal tab or restart your terminal so the CLI picks up the latest config. Claude Code’s docs also recommend restarting for a cleaner flow when using /login.
Set DeepSeek v3.1 as default. For frontend refactors or large‑file analysis, switch to Qwen Coder with one click; for complex reasoning, switch back to DeepSeek. No hand‑editing configs at any point.
Already using Codex or Claude Code? CC‑Switch automatically imports your current primary config as the default provider copy so you always have a “home base” to return to.
When debugging goes sideways or quota is tight, switch back to Claude official sign‑in or Codex Sign in with ChatGPT. Avoid detours and get things done first.
Codex config files
The community tends to use ~/.codex/config.toml, but some mention historical versions/installs leading to different filenames or missing files (e.g., only seeing config.json). During switching, CC‑Switch creates an empty config.toml as a fallback to keep things consistent.
Claude Code’s official path
Official docs state the user‑level config is ~/.claude/settings.json. If your older environment still uses claude.json, CC‑Switch supports that too.
Privacy and security
CC‑Switch only modifies local files and does not send anything to a server. For team sharing, manage project‑level .claude/settings.json etc. with Git yourself (Claude Code also supports project‑level settings).
There are command‑line tools for provider switching, but CC‑Switch’sGUI plus “copy strategy” is more intuitive and robust.
If you want minimalism, visualization, and one‑click fallback to official sign‑in, it’s a great “always‑on helper.” (It complements CLI/scripts.)
(Reference: There are similar community tools and posts, but CC‑Switch focuses more on “GUI + dual ecosystems”.)
Q1: If switching fails, can I roll back?
Yes. The currently “in‑use” primary config is first backed up to that provider’s copy, so you can switch back to it anytime.
Q2: Codex keeps asking me to sign in or says the config is invalid.
Prefer “Codex Official Sign‑In” (Sign in with ChatGPT) first, or confirm OPENAI_API_KEY and OPENAI_BASE_URL are in the correct places within auth.json/config.toml.
Q3: Why is the app so small and fast to start?
Because Tauri bundles only the necessary native shell and frontend assets; the official guides emphasize the small‑bundle approach. After migrating to Tauri 2.0, the reported improvements are significant.
Turn “switching providers” into a single click, and you can focus on what really matters: writing the right code and making the right decisions.
For people who collaborate across models, CC‑Switch is like a gear shifter on your desktop — handy, reliable, and fast.
Give v3.1.0 a spin and let your multi‑provider workflow take off.
Gemini for discovery → ChatGPT for planning and review → Claude Code to generate and execute code → ChatGPT for post-PR review → Gemini for synthesis and knowledge capture. This “division-of-labor” chain connects think → build → review → codify in one loop.
Exploration → Gemini: diverge fast, surface patterns you overlooked.
Refinement → ChatGPT: turn insights into executable steps and check for logical gaps.
Integration → Claude Code: implement, polish, and ensure narrative/architecture coherence.
Example: for a product proposal, ask Gemini for deep needs → have ChatGPT list the functional spec → ask Claude Code to assemble a complete, shippable document or PR.
Summary of strengths: Gemini is fast with strong intuition; ChatGPT excels at reasoning; Claude is excellent at structure and clear expression. This also aligns with recent positioning: Google frames Gemini 2.5 as “thinking models” with fast/efficient Flash and stronger Pro; OpenAI highlights ChatGPT’s reasoning and planning for deep research; Anthropic pushes Claude Code as a hands-on terminal agent that can read repos, change code, and open PRs.
Gemini: “intuition radar”
Native multimodal and thinking-model design → fast and broad coverage. Great for opening up the problem and pulling on promising threads. With 2.5 you can dial thinking budget to go broad first, then deepen.
ChatGPT: structured planning and deep reasoning
Strong at logical decomposition, argumentation, and integrating external sources — ideal for technical plans, tradeoff analysis, and review checklists.
Claude Code: gets things done
Built as a terminal-first coding agent: read the repo, change code, run tasks, open PRs and trigger CI. Short path from plan to PR.
Note: model strengths shift over time. Treat this split as pragmatic defaults, not hard rules. Adjust per project type.
A day-to-day development loop (with a realistic example)
Goal: surface true user pains behind “subscription management”, and produce journeys/boundary conditions.
Example prompt (to Gemini)
“We’re adding ‘subscription management’. From four angles — user segments (new/active/churn-risk), key motivations, cancel/retention touchpoints, and regulatory/payment compliance — map potential needs. Provide 3 high-leverage hypotheses we likely miss, each with a verifiable event and a quantifiable metric.”
Why Gemini: fast divergence/enumeration and adjustable “thinking budget” → go broad first, then narrow.
Goal: write code, change code, run tasks, open a PR.
How to use:
Install @anthropic-ai/claude-code, enter your repo, and chat; Claude will index the repo and resolve dependencies.
In PRs/Issues, mention @claude to generate or modify code and create a PR.
Example command (conceptual)
Terminal window
# At repo root
claude"Create backend event tables for subscription management, add migrations, and integrate tests; if a Stripe Webhook exists, mount a handler. Then open a feature branch and submit a PR."
The value is end-to-end: read code, modify code, run scripts, and drive the Git flow in one loop.
Goal: probe for issues and fix them.
Give ChatGPT the Claude-generated PR and ask for a checklist across complexity, testability, rollback strategy, and failure paths, plus concrete Diff suggestions.
ChatGPT’s recent deep-research/long-chain reasoning is well-suited to serve as a contrarian reviewer.
Document the split in the repo
In CONTRIBUTING.md, codify the default loop: discover → plan → implement → review → synthesize. Reduce context-switching.
Wire actions into CI/CD
Enable Claude Code GitHub Actions to trigger PR generation/modification from comments.
Mobile or Android projects
Use Gemini Code Assist inside the IDE for error dissection and fix suggestions.
Mind safety and compliance
Follow Google’s GenAI safety guidance: data minimization, explainability, and robust rollback.
Copyable loop checklist (paste into your team wiki)
Treat models like people with roles, not a generic toolbox.
Send the fast-intuitive one to explore, the deliberative one to plan, the hands-on one to ship. When each step has an “owner”, your delivery rhythm flows.
Would this split work for you? How would you tweak it? Drop thoughts in the comments.
Zed turns “agents” into a standard interface (ACP) and invites your command‑line agents into the editor. Gemini CLI is first‑class; Claude Code can run in Zed via a community ACP bridge. Multi‑agent coexistence is the new normal.
Editor–Agent decoupling
Like LSP for languages, ACP lets you choose which agent plays. In the same project, switch agents for different tasks anytime.
Real CLI power, visualized in the editor
Zed spawns real Gemini CLI subprocesses and speaks JSON‑RPC. You get all CLI capabilities plus live change visualization and multi‑buffer reviews.
Open spec and growing ecosystem
ACP is open (Apache). The Neovim community is also adding ACP‑compatible agents. This isn’t single‑editor locked.
You can also use Claude Code in Zed via the community ACP bridge acp-claude-code. Add this to your Zed settings (auto‑accept edits supported):
{
"agent_servers": {
"claude-code": {
"command": "npx",
"args": ["acp-claude-code"],
"env": {
"ACP_PERMISSION_MODE": "acceptEdits"
}
}
}
}
The bridge implements an ACP agent that wraps the Claude Code SDK: session continuity, streaming output, tool calls, permission modes, and more. The Zed team notes native support is in progress; use the bridge for now.
Note: acceptEdits only auto‑accepts file edits. bypassPermissions skips all prompts — not recommended unless running batch jobs in a clean sandbox.
Everyone is trying Claude Code lately. The problem: your company network/budget doesn’t allow direct overseas access. Now what?
Good news: multiple top China providers offer Anthropic-compatible endpoints. Change Claude Code’s Base URL + Key and you’re in. This guide walks you through DeepSeek, Zhipu GLM-4.5, and Kimi K2, plus a quick “gotchas” checklist.
# Some deployments map temperature to ~0.6; defaults are fine
Moonshot emphasize compatibility with both OpenAI and Anthropic for K2 in the repo and blog. Community guides commonly use https://api.moonshot.ai/anthropic as the Base URL. Note: some posts show .cn which can fail. Prefer .ai or check the console docs.
ANTHROPIC_AUTH_TOKEN (sent as Authorization: Bearer)
China-compatible endpoints generally prefer ANTHROPIC_AUTH_TOKEN together with a custom Base URL.
Q2: Can’t connect / 404?
Ensure the Base URL includes /anthropic (e.g., DeepSeek and Moonshot compatibility endpoints).
Use .ai for Kimi; some .cn endpoints may not work.
Q3: Built-in WebSearch tool doesn’t respond?
When not using Anthropic’s official API, the built-in WebSearch tool is not available. Use an external MCP search tool (Tavily/Brave/DuckDuckGo, etc.).
With domestic models connected, Claude Code remains the same “turn ideas into code” engine. Which one feels best? DeepSeek is fast, GLM-4.5 thinks steadily, and K2 is great value. Try them side by side.
Once you have one path working, your team can pilot low-cost AI code collaboration and turn efficiency into real outcomes.
Which combo works best for you? Any pitfalls you’ve hit? Share your practices in the comments.
GUIs are great, but the command line is faster, steadier, and composable. Claude Code CLI is that trusty knife: open a REPL, one-shot via pipes, resume past sessions, and fine-tune permissions — all with a single command.
This guide covers 90% of daily CLI usage with minimal memorization. If you can copy it, don’t type it. If you can automate it, don’t do it by hand.
claude--append-system-prompt"You are a careful code reviewer."
Mindset: REPL is perfect for exploratory work, incremental refactors, and step-by-step walkthroughs. Switch models on the fly with --model sonnet (or a full model ID).
What does --replay-user-messages do? In streamed JSON pipelines, it replays stdin “user” messages to stdout so middleware can align event flows upstream and downstream.
The CLI shines for two reasons: speed and composability. Use REPL for exploration; use -p for automation and pipelines. With traceable sessions, controllable permissions, and orchestratable output formats, Claude Code fits naturally into your engineering rhythm.
Type 10× fewer commands, deliver 10× more results.
Which combo works best for you? Share your “command flows” in the comments — I’ll turn a few into posters and send you a Vibe Coding quick-start pack.
Teach Claude Code to “pick up the thread” with the quartet of --continue / --resume / --session-id / non-interactive mode, so you can keep previous context intact, coordinate multiple tasks, and automate reliably.
Writing code is like training — the worst enemy is interruption. You discussed a refactor plan with Claude Code yesterday, and today you forget where you left off? Running multiple threads gets mixed up? Your script loses context after two turns?
This article explains how Claude Code sessions work and how to resume instantly, recover specific threads, run many in parallel, and keep context in scripts — so your Vibe Coding rhythm never drops.
This shows a list of past sessions (start time, first prompt or summary, message count). Use arrow keys to choose. Great when you switch tracks several times a day.
Resume directly by session ID:
Terminal window
claude--resume550e8400-e29b-41d4-a716-446655440000"Finish the API abstraction layer"
Idea: start once to capture a session_id, then chain every subsequent step with --resume $sid.
Terminal window
# Step 1: start a session and grab session_id (present in JSON output)
sid=$(claude-p"Start a refactor session: list the risk points"--output-formatjson|jq-r'.session_id')
# Step 2: chain non-interactive turns
claude-p--resume"$sid""According to the plan, refactor utils/date.ts first"
claude-p--resume"$sid""Add ts-jest config and unit test examples"
Key point: --output-format json includes a session_id field; you can also use --no-interactive with --resume for strictly non-interactive runs.
Caution: some users report that repeatedly using --continue in non-interactive mode may create a new session, which looks like it resumed but actually changed IDs. For automation, prefer fixed session_id + --resume.
You’ll get a list of sessions to restore. This is the same source as --resume/--continue, triggering the same SessionStart/Resume logic (as noted in Hooks docs).
Handy REPL keys:
Esc Esc: edit the previous message
↑ / ↓: browse history in the current session
Great for quickly tweaking your last prompt.
Parallel, Not Cross-Talk: isolate sessions with Git worktrees
Want to push “bugfix” and “new feature” at the same time? Use git worktree to create two working directories. Launch Claude Code separately in each directory so context stays naturally isolated:
Terminal window
gitworktreeadd../proj-feature-a-bfeature-a
gitworktreeadd../proj-bugfixbugfix-123
cd../proj-feature-a && claude
cd../proj-bugfix && claude
This workflow is strongly recommended: sessions and file state won’t affect each other.
Privacy & Cost: session retention and background summaries
Local retention: the client stores sessions locally (configurable; common defaults keep ~30 days) for --resume. Enterprises can set zero data retention.
Background summarization: to power --resume lists and previews, Claude Code performs background session summarization, incurring a small token cost.
If you’ve configured Hooks such as SessionStart/SessionEnd, resuming a session also triggers them. You can automatically load the latest issues, change summaries, or safety checks at resume time.
Session management isn’t a nice-to-have — it’s the foundation for keeping momentum. Use --continue for quick, ad-hoc resumption; use --resume + session_id for long-running, replayable work; use worktree for physical isolation; and bring it all into scripts. Your Vibe Coding will evolve from sketchy inspiration to a reproducible creative pipeline.
Which session pattern do you need most right now? Share in the comments.
Keep getting interrupted by permission prompts?
Want Claude Code to run in CI/CD, nightly jobs, or remote containers without flipping “allow everything”?
The answer is this switch: --permission-prompt-tool.
It delegates the decision of “allow this tool call?” to a specific MCP tool you choose. It supports conditional approval, audit trails, and can run in non-interactive CLI and headless setups. The CLI docs explicitly state its purpose: in non-interactive mode, your MCP tool handles permission prompts.
Permission order is also clear: first check settings.json / --allowedTools / --disallowedTools. If a rule applies, it’s allowed or denied immediately. Otherwise Claude calls your --permission-prompt-tool. The official SDK docs describe the full flow and the JSON response format.
What it is: a bridge that delegates “allow or deny tool use” to an MCP tool. Great for automation, headless, and compliance.
How decisions are made: static rules first (settings/allowed/disallowed). If no match, Claude calls your permission agent, which returns JSON: allow or deny.
Best use cases: CI/CD, batch refactors, remote containers/servers, IDE/plugins, enterprise audit/compliance.
Why sec teams like it: auditable, recordable, granular, aligned with IAM and least privilege. Even better with an MCP allowlist.
It forwards the question “should we allow this tool call?” to your MCP tool.
That MCP tool can enforce approvals, allow/deny lists, time-of-day policies, Slack approvals, risk scoring—your rules, your control. This works in both CLI and headless modes.
Claude Code applies a three-layer permission check:
--dangerously-skip-permissions: bluntly skips all prompts. The docs warn to use sparingly.
--permission-prompt-tool: controlled, auditable, and extensible. Aligns with enterprise security/compliance. You can authorize by directory/command, e.g., Bash(npm run test:*), Edit(docs/**).
--permission-prompt-tool is not just a toggle—it’s an enterprise-grade “permission gateway” interface.
You can move risk evaluation, human approvals, and audit trails into a single MCP tool.
Automation without recklessness: keep both efficiency and security strong.
Does this permission design satisfy your compliance needs?
Tell us how you’d wire Slack/Teams/ticketing to “click-to-approve”.
In recent years, AI coding assistants have proliferated. Among them, Anthropic’s Claude Code (CC) has drawn attention by positioning itself as an “intelligent coding tool in your terminal.”
“Vibe Coding” refers to using these tools to rapidly turn ideas in your head into code — a fast‑iterating, intuition‑driven development flow.
This article distills feedback from different types of users, explains the reasons behind the patterns, and summarizes where Claude Code shines in Vibe Coding. We also compare CC to adjacent tools (Cursor, GitHub Copilot, OpenAI Codex CLI, Cursor CLI, Google Gemini CLI, etc.) to highlight strengths, weaknesses, and fit.
Dev teams: internal integrations and LLM experiments.
Less suited:
Complete beginners (barrier too high).
Senior architects who need rigor and determinism (use CC as an aide, not a driver).
Claude Code isn’t a silver bullet, but in the Vibe Coding arena it helps more ideas leap from mind to reality. The winner of this AI developer‑productivity race will be whoever best balances cost with reliability.
Claude Code pushes the long‑running trend of “write less code, get more done” to the extreme. Writing code is only a slice of engineering. Dialogue and collaboration help both “non‑coders” and engineers convert Unknown Unknowns into Known Unknowns — that’s how Claude Code becomes real productivity.
A discussion has been making the rounds: some say LLMs let “non‑coders” ship small software; professional engineers counter that software engineering is much more than code — requirements, architecture, integration, and long‑term maintenance. Both sides are right — just speaking from different vantage points. Behind the debate is a decades‑long industry arc of “writing less code,” and Claude Code is simply the next turn of the crank.
Because this time AI offers them a usable interface:
PMs can spin up prototypes in natural language and surface ambiguities earlier.
Ops/design can tweak small features or scripts directly without waiting in backlogs.
Media are observing the same trend. “Vibe Coding” — programming closer to intent, less fixation on code — is heating up. Reporters have done pair‑coding sessions and, in two days, shipped small UI and feature changes. The term took off in early 2025.
Architectural boundaries, data contracts, performance budgets, observability — none of these are solved by “a couple snippets.”
Tests and acceptance criteria must be explicit. Tools like Cucumber/Gherkin let you write business‑readable, executable acceptance tests that AI can also help generate and maintain.
In short: AI can help us write less code, but the engineering organization and constraints must be stronger.
Bringing “non‑coders” into the loop is good — they sit close to the business and push 0→1. Having engineers own boundaries and constraints is necessary — that’s how systems run long and run stable. Arguments won’t close the loop; collaboration will.
Next step: take a 3–5 day feature in your product and run it through the collaboration playbook above. After a retro, decide how far to open the gates for Claude Code.
Cover: a PM and an engineer high‑five in front of a terminal; the screen shows CLI, PRs, Gherkin scenario cards, and an AI helper icon; warm tech illustration with ample whitespace.
Key sections:
“History of writing less code”: icons for Rails scaffolding, Proto contracts, Scala given/implicit on a timeline.
“Claude Code capabilities”: terminal UI + file tree + Git commit arrows + connectors to Jira/Drive.
“Collaboration playbook”: five cards (Acceptance → Prototype → CI → Contracts → Retro) arranged like a kanban.
This nails the real friction: AI doesn’t replace engineering — it amplifies collaboration. Saving this and running a small feature with @claude + Gherkin.
Just enabled @claude + Gherkin in our repo — speed unlocked. From now on: write acceptance first, then code.
Non‑coders × engineers is the winning combo. Next time the “replacement” debate comes up, I’ll share this piece.
Mention @claude right inside PRs, Issues, and scheduled jobs. It follows your repo’s CLAUDE.md to review code, fix bugs, implement small features, and even post daily reports — all on GitHub‑hosted runners with zero server ops.
Ever felt this? PRs pile up and reviews lag; Issues are clearly described, yet no one wants to take the first swing.
With Claude Code’s GitHub Actions, a single @claude hands off those repetitive but necessary tasks. It reads context, adheres to your project conventions, and can create or update PRs — even propose implementations.
Open the Claude app in your terminal and run: /install-github-app.
Follow prompts to install the GitHub App and configure the ANTHROPIC_API_KEY secret.
Test by mentioning @claude in a repo comment.
If that path doesn’t work, use manual install: install the official GitHub App → add ANTHROPIC_API_KEY to the repo → copy examples/claude.yml to .github/workflows/.
Behavior: when someone comments @claude this code may have a concurrency issue; suggest and fix, the Action reads context, replies, and, if appropriate, commits changes.
The busier your team, the more you benefit from a reliable “automated teammate.” Put conventions in CLAUDE.md and workflows in GitHub Actions, and hand the tedious yet critical chores to Claude — freeing people to create higher‑value work.
Which workflow will you hand to @claude first? Share your ideas or pass this guide to a teammate and try it today.
Cover: a developer types “@claude” in a GitHub PR while an AI assistant materializes task cards like “Review”, “Fix tests”, and “Create PR”; clean pastels, minimal UI, soft shadows, 2:1.
Section art:
Flow: GitHub comment → Claude analysis → PR update; flat modern arrows and cards, 2:1.
A CLAUDE.md style guide pinned with checkmarks for security, tests, performance; 2:1.
Bedrock & Vertex AI icons connecting to GitHub Actions runners; compliance shield; 2:1.
Scenario: Claude Code runs out of quota right when you need it, but you happen to have a GitHub Copilot subscription.
By using an open-source Copilot API proxy that exposes Copilot as an OpenAI/Anthropic compatible API, you can plug Claude Code into it and keep Vibe Coding. This guide walks you through setup and recommends safer, more compliant usage.
This is a reverse‑engineered GitHub Copilot API proxy that turns Copilot into an OpenAI-compatible /v1/chat/completions service and an Anthropic-compatible /v1/messages service. The upside: any tool supporting those APIs can use it—including Claude Code. The project ships with usage dashboards, rate limiting, manual approval, and support for Business/Enterprise accounts.
Note: This is not officially supported by GitHub. GitHub explicitly restricts excessive automation/bulk requests, which may trigger risk controls or temporary suspension. Use at your own risk and stay reasonable.
Prerequisite: you have an active GitHub Copilot subscription.
Terminal window
# 1) Interactive login
npxcopilot-api@latestauth
# 2) Start the proxy and generate a Claude Code connect command
npxcopilot-api@lateststart--claude-code
# 3) Paste the suggested command in a new terminal (or set env vars manually)
ANTHROPIC_API_KEY=sk-dummy\
ANTHROPIC_BASE_URL=http://localhost:4141 \
claude --dangerously-skip-permissions
The above flow comes from the built‑in --claude-code wizard. You can also persist the environment via .claude/settings.json to avoid exporting variables every time.
More robust approach: avoid using --dangerously-skip-permissions long‑term. Prefer Claude Code’s permissions and environment settings—configure ANTHROPIC_BASE_URL, default model, and a permissions allowlist in the project’s .claude/settings.json to prevent mistakes.
Good fit: individual developers and small teams with a paid Copilot plan who want a smooth backup when Claude Code quota runs low.
Not ideal: strict enterprise compliance environments—prefer official Claude Code integrations (e.g., Bedrock/Vertex/LLM gateways/enterprise proxies).
[Illustration suggestion | 2:1]
Theme: left side “indie desk setup”, right side “enterprise server room” with a compliance shield and cloud provider badges.
English prompt: Split-screen illustration: indie desk setup vs enterprise server room with a compliance shield. Minimal, 2:1.
Think of it as an emergency power pack. When timing matters, it keeps your Vibe Coding flow alive; day‑to‑day, it can serve as a low‑cost online backup. But remember—stable, compliant, least privilege is the productivity that lets you sleep well.
Will you make it your long‑term safety net, or keep it for emergencies only? What do you think?
Over the past two months I’ve become addicted to Vibe Coding.
Pair Spokenly for voice input with Claude Code as a “capable terminal copilot”.
I keep three panes open: Plan, Build, Review.
I grab tea; when I’m back it’s just “acceptance” and “next steps”.
Before bed I toss in a big task; next morning I check results like a ruthless boss — day’s rhythm, locked.
This guide shows the complete setup for driving Claude Code by voice on macOS with Spokenly. Install and go — you can ship tonight.
Spokenly: global hotkey, dictation in any input field; supports local Whisper, offline-ready; or bring your own cloud API key; Agent Mode to control your Mac by voice.
Claude Code: smart pair programmer in your terminal. After npm i -g @anthropic-ai/claude-code, just run claude; supports slash commands like /model /config /ide /compact, deep IDE integration with VS Code/JetBrains.
Voice tips: use Option+Enter or /terminal-setup for multi-line input; Esc Esc to edit the last message; run three-pane flow for Plan → Execute → Review.
Break big tasks into four spoken parts so Claude Code stays on track:
Goal: one-sentence objective + success criteria
Scope: which directories to touch and which to avoid (allow/deny lists)
Checks: self-test scripts, lint, unit/E2E tests
Deliverables: PR summary, diff, changelog
Example:
“Tonight migrate the Settlement module from REST to tRPC. Only modify /server/ and /app/billing/. Run pnpm test and pnpm lint — both must pass. Output steps and risks, and provide a rollback guide.”
Voice-driving Claude Code isn’t a gimmick — it helps you reach the “clarify → deliver” state faster.
The clearer you describe requirements, the steadier Claude executes — and the better the Vibe.
Try the three-pane method tonight and let it handle your pre-sleep ticket.
Where does this workflow save you the most time? Share your take.
As reported by Bloomberg on August 22, 2025, leading AI startup Anthropic is reportedly close to securing a massive funding round of up to $10 billion — far above prior expectations and among the largest single financing rounds in the AI sector to date.
Based on a comprehensive review of the “Anthropic nearing a $10 billion raise” reports, this analysis applies multi‑source fact‑checking, data validation, and critical reasoning to assess the story’s truthfulness and reliability. We cross‑checked at least 15 sources and found several discrepancies and points of concern. Below, we examine factual verification, data analysis, logical consistency, incentives, market psychology, and potential risks.
Cross‑referencing Bloomberg, CNBC, TechCrunch, Reuters, and other outlets confirms that Anthropic is indeed negotiating a new funding round, but key details vary. Early reporting (e.g., Bloomberg) cited a $3–5 billion raise at a roughly $170 billion valuation, not the $10 billion some outlets repeated.
A more realistic timeline looks like this:
2025‑07‑29: Bloomberg first reported Anthropic’s talks for a $3–5B round at a ~$170B valuation, led by Iconiq Capital, with a potential second lead. The company was also said to be engaging the Qatar Investment Authority (QIA) and Singapore’s GIC for participation.
2025‑08‑20/21: Several outlets, including Bloomberg, updated that the target could rise to “as much as $10B,” potentially becoming one of the largest AI startup financings ever. Anonymous sources cited strong investor demand pushing the target from $5B to $10B.
Investor roster: Iconiq Capital as lead appears consistent across reports, but other names vary. Some mention TPG, Lightspeed, Spark Capital, Menlo Ventures, and continued outreach to QIA and GIC. These discrepancies suggest no single official confirmation yet.
Conclusion: A new Anthropic raise is real, but the size and full investor lineup remain fluid. The “doubling” from $5B to $10B hinges on anonymous sourcing and lacks formal confirmation.
Cash burn: Raising $3.5B in March and then targeting $5–10B just five months later implies very heavy spend — potentially hundreds of millions to billions per month.
Revenue multiple: Anthropic’s valuation/revenue multiple of ~34× vs OpenAI’s ~25× suggests a premium that’s hard to justify without clear differentiation.
QIA and GIC involvement introduces geopolitical and national‑security review complexity.
Management has previously voiced concerns about optics and regulatory scrutiny tied to Middle East capital, though capital intensity may force pragmatism.
Prioritize actual revenue and profitability over valuation theater.
Beware bubble dynamics and momentum herding.
Wait for official confirmation before anchoring decisions.
In today’s AI investment cycle, media narratives, corporate signaling, and on‑the‑ground reality often diverge. In moments of mania, noise overwhelms signal. Only disciplined verification and clear‑eyed analysis can keep decisions grounded in truth instead of sentiment.
Spin up a “sub‑agent” inside Claude Code dedicated to calling the Gemini CLI. Offload heavy code scans, architecture overviews, and security audits to Gemini’s 1M‑token context; keep Claude as the brain to interpret results and land changes. Cheaper, simpler, and faster.
For repository‑wide overviews, cross‑repo search, and architecture mapping, even Claude can get bottlenecked by context size.
The fix is simple: outsource large‑mouth analysis to the Gemini CLI. We’ll create a Gemini analysis subagent in Claude Code that only does three things: build commands → call the CLI → hand raw results back. Claude stays unpolluted and focused on decisions and edits — a powerful pairing.
Why now?
Google open‑sourced the Gemini CLI. Personal accounts can connect to Gemini 2.5 Pro for free, with a 1M‑token context and generous preview quota. For whole‑repo analysis, it’s basically a “power‑up.”
Figure suggestion (2:1 illustration): An engineer orchestrates two robots at a console: left “Claude (planner/editor)”, right “Gemini subagent” ingesting a massive code pile. Use English UI labels only.
Claude Code lets you define subagents as Markdown with YAML frontmatter. At the project level, place them under .claude/agents/ (user‑level: ~/.claude/agents/).
Create: .claude/agents/gemini-analyzer.md
---
name: gemini-analyzer
description: Manages Gemini CLI for large codebase analysis and pattern detection. Use proactively when Claude needs to analyze extensive code patterns, architectural overviews, or search through large codebases efficiently.
tools: Bash, Read, Write
---
You are a Gemini CLI manager specialized in delegating complex codebase analysis tasks to the Gemini CLI tool.
Your sole responsibility is to:
1. Receive analysis requests from Claude
2. Format appropriate Gemini CLI commands
3. Execute the Gemini CLI with proper parameters
4. Return the results back to Claude
5. NEVER perform the actual analysis yourself - only manage the Gemini CLI
When invoked:
1. Understand the analysis request (patterns to find, architectural questions, etc.)
2. Determine the appropriate Gemini CLI flags and parameters:
- Use `--all-files` for comprehensive codebase analysis
- Use specific prompts that focus on the requested analysis
- Consider using `--yolo` mode for non-destructive analysis tasks
3. Execute the Gemini CLI command with the constructed prompt
4. Return the raw output from Gemini CLI to Claude without modification
5. Do NOT attempt to interpret, analyze, or act on the results
Example workflow:
- Request: "Find all authentication patterns in the codebase"
- Action: `gemini --all-files -p "Analyze this codebase and identify all authentication patterns, including login flows, token handling, and access control mechanisms. Focus on the implementation details and architectural patterns used."`
- Output: Return Gemini's analysis directly to Claude
Key principles:
- You are a CLI wrapper, not an analyst
- Always use the most appropriate Gemini CLI flags for the task
- Return complete, unfiltered results
- Let Claude handle interpretation and follow-up actions
- Focus on efficient command construction and execution
## Detailed Examples by Use Case
### 1. Pattern Detection
**Request**: "Find all React hooks usage patterns"
**Command**: `gemini --all-files -p "Analyze this codebase and identify all React hooks usage patterns. Show how useState, useEffect, useContext, and custom hooks are being used. Include examples of best practices and potential issues."`
**Request**: "Locate all database query patterns"
**Command**: `gemini --all-files -p "Find all database query patterns in this codebase. Include SQL queries, ORM usage, connection handling, and any database-related utilities. Show the different approaches used."`
### 2. Architecture Analysis
**Request**: "Provide an architectural overview of the application"
**Command**: `gemini --all-files -p "Analyze the overall architecture of this application. Identify the main components, data flow, directory structure, key patterns, and how different parts of the system interact. Focus on high-level organization and design decisions."`
**Request**: "Analyze the component hierarchy and structure"
**Command**: `gemini --all-files -p "Examine the React component hierarchy and structure. Identify reusable components, layout patterns, prop drilling, state management approaches, and component composition patterns used throughout the application."`
**Command**: `gemini --all-files -p "Analyze this codebase for potential performance bottlenecks. Look for expensive operations, inefficient data structures, unnecessary re-renders, large bundle sizes, and optimization opportunities."`
**Request**: "Identify security vulnerabilities"
**Command**: `gemini --all-files -p "Scan this codebase for potential security vulnerabilities. Look for authentication issues, input validation problems, XSS vulnerabilities, unsafe data handling, and security best practices violations."`
### 4. Technology Stack Analysis
**Request**: "Identify all third-party dependencies and their usage"
**Command**: `gemini --all-files -p "Analyze all third-party dependencies and libraries used in this project. Show how each major dependency is utilized, identify any potential redundancies, outdated packages, or security concerns."`
**Request**: "Map out the testing strategy and coverage"
**Command**: `gemini --all-files -p "Examine the testing strategy used in this codebase. Identify test frameworks, testing patterns, test coverage areas, mocking strategies, and areas that might need more testing."`
### 5. Feature Analysis
**Request**: "Trace a specific feature implementation"
**Command**: `gemini --all-files -p "Trace the implementation of [specific feature] throughout the codebase. Show all files involved, data flow, API endpoints, UI components, and how the feature integrates with the rest of the system."`
**Request**: "Find all API endpoints and their usage"
**Command**: `gemini --all-files -p "Catalog all API endpoints in this application. Include REST routes, GraphQL resolvers, tRPC procedures, their request/response patterns, authentication requirements, and how they're consumed by the frontend."`
### 6. Migration and Refactoring Analysis
**Request**: "Identify legacy code patterns that need modernization"
**Command**: `gemini --all-files -p "Identify outdated or legacy code patterns that could be modernized. Look for old React patterns, deprecated APIs, inefficient implementations, and opportunities to use newer language features."`
**Request**: "Analyze consistency across similar components"
**Command**: `gemini --all-files -p "Examine similar components or modules for consistency. Identify variations in patterns, naming conventions, implementation approaches, and opportunities for standardization or creating reusable abstractions."`
**Command**: `gemini --all-files -p "Analyze this codebase to help create onboarding documentation. Identify key concepts developers need to understand, important files and directories, setup requirements, and the most critical patterns to learn first."`
### Command Flag Guidelines:
- Always use `--all-files` for comprehensive analysis
- Add `--yolo` for non-destructive analysis tasks to skip confirmations
- Use `-p` for single prompts or `-i` for interactive sessions
- Consider `--debug` if you need to troubleshoot Gemini CLI issues
Subagent file format, placement, and available tools (Bash/Read/Write) follow the official docs.
Tip: You can also create/edit subagents interactively via /agents.
Permissions and Safety: “Green‑light” the Subagent
Usage: talk to the primary agent in Claude Code, e.g., “Have the gemini-analyzer subagent scan the whole repo’s authentication implementation and return the raw output.”
Use the gemini-analyzer subagent to analyze authentication patterns across the repo and return raw output.
Subagent command (example)
Terminal window
gemini-p"Analyze this codebase and list all authentication & authorization patterns: login flows, token/session handling, middleware/guards, RBAC/ABAC, and any security pitfalls. Show file paths and code excerpts when helpful."
-p runs a non‑interactive, one‑shot analysis. Run at the repo root so Gemini reads the project context; optionally add --include-directories for extras.
2) Architecture bird’s‑eye: components and data flow
Use gemini-analyzer to provide a high‑level architectural overview and return the raw result.
Subagent command
Terminal window
gemini-p"Give an architectural overview: main modules, data flow, directory structure, cross-cutting concerns, and how backend APIs connect to frontend components. Output a concise map with file references."
Large context is ideal for whole‑repo maps and cross‑layer traces — perfect for the Gemini CLI’s 1M tokens.
This Claude brain + Gemini subagent combo is ideal for whole‑repo sweeps, architecture health checks, migration assessments, and dependency cleanup.
You stay in Claude Code and hand heavy lifting to Gemini. You save money and time. The next time you need “wide view + grounded action,” run them together.
Where would you use this? Share your scenarios — I can help further refine the subagent prompts for your case.
This year, Silicon Valley gave a name to a style of coding: Vibe Coding.
It’s not slacking off, and it’s not “let the AI do everything.”
It’s using Claude Code as a “disciplined junior engineer” so teams ship faster and more reliably.
For each feature, enter plan‑mode once, generate a task list, then execute against that list. Stability improves dramatically. Community experience shows this “rigid sequence” shifts Claude from “occasionally off the rails” to “reliably intern‑like.”
/implement <feature> to commit a PR strictly by the task list
/context to inspect context usage and tool calls
Official support includes custom commands and namespaces, so you can bake the team process into the repo.
The direction is clear: developers are rapidly embracing AI, and automation in coding/testing is rising. Teams who wield AI well will compound their advantage.
To win on quality, you must bake engineering process into Claude’s commands and plans — not “chat casually and let it write.”
Vibe Coding isn’t laziness.
It’s “human judgment + Claude execution” assembled into a measurable, auditable, reusable pipeline.
Starting today, give your team: design first → one‑time plan‑mode → tests first → slash‑command codification → security + review to close. Fast and steady.
What’s missing from this pipeline for your team? Share your experience in the comments.
In AI coding, two things are most feared: context evaporation and parallel chaos.
The result is rework, conflicts, and invisible progress.
ccpm’s approach is very “engineering-oriented”: use GitHub Issues as the single source of truth, where every line of code can be traced back to specifications. Claude only handles execution, while state, dependencies, and auditing all reside in Issues, allowing the entire team to seamlessly hand off work.
Core Philosophy: No Vibe Coding - all code must be traceable to clear specifications.
Specification-Driven: Structurally decompose PRDs into Epic/Tasks, then sync to GitHub Issues. Full traceability across the chain.
Parallel Execution: One Issue isn’t one person, but multiple parallel workflows (DB, API, UI, testing…) progressing concurrently in isolated worktrees.
Context Preservation: Main thread only orchestrates; each agent works in its own small context without mutual contamination.
GitHub Native Collaboration: Issue = project status; comments = audit trail; multiple humans/AI agents can hand off in real-time without “what did AI do” meetings.
Bonus: ccpm recommends installing the gh-sub-issue extension to manage parent-child tasks; without the extension, it automatically falls back to task lists and still works.
I summarize ccpm’s definition in 8 characters: specifications grounded, parallelism ordered.
Not relying on slogans, but on commands and deliverables, speaking through action.
If you’re stuck with “lost context, parallel conflicts, invisible progress,” it’s worth trying immediately.
Which type of problem do you most want to solve with ccpm? Let’s chat in the comments.
Many people install Claude Code but only use it from an external terminal.
In fact, it already integrates with mainstream IDEs: Cursor / VS Code / Windsurf / VSCodium / the JetBrains family.
The benefits are immediate: one‑key summon, in‑IDE diff view, automatic sharing of selections and diagnostics — and after changes, you can review them with the IDE’s native comparison tools.
VS Code and its variants will auto‑install the extension, then you can use everything listed above inside the IDE. If you start from an external terminal, type /ide to connect Claude Code to the current IDE.
Make sure the CLI works
VS Code: run Shell Command: Install ‘code’ command in PATH from the command palette. This stabilizes code . and IDE detection for Claude.
Cursor: run Install ‘cursor’ to shell from the command palette (you can also install code). If PATH ordering looks off, adjust priority.
Common config
Run /config → set Diff tool to auto to use the IDE’s diff view automatically.
From any external terminal, you can still use /ide to bind to the IDE session.
PR‑driven: define the requirement → run claude on a branch to plan and make changes → review diffs in the IDE → test locally → open a PR.
Pair‑Agent: humans steer the plan; Claude executes; the IDE shows diffs and diagnostics in real time.
Same root, same context: always start from the project root so everyone shares identical paths and context.
The above blends official “best practices” with our daily experience.
Installing the tool isn’t the goal — closing the human‑AI loop inside the IDE is.
Today, connect Claude Code to your Cursor/VS Code/JetBrains and try the full loop: quick summon → in‑IDE diff → diagnostics sharing → PR.
You’ll likely find: cleaner changes, fewer words, faster cadence.
Question: Where does your team struggle more — “understanding legacy code” or “safely changing a big module”? Which extra step would you want Claude to take inside the IDE? Share your thoughts.
Uzi is a command-line tool that uses Git worktree + tmux to run multiple AI coding agents in parallel, each with its own dependencies and development port, then uses checkpoint to merge results to the main branch with one command.
One agent slowly modifying pages, another simultaneously refactoring the backend, and a third writing tests.
Multi-threaded progress efficiency far exceeds “queuing for models”. This is how Uzi works: same repository, branch isolation, parallel execution, visual monitoring, one-click merging. The official README provides complete installation, configuration, and workflow examples, with v0.0.2 released on 2025-06-03.
High-Concurrency Experimentation: Start 3-5 agents at once, each exploring different implementations or styles, then merge the best solution.
True “Isolation”: Uses Git worktree to give each agent an independent branch and directory, preventing cross-contamination.
Automatic Development Environment: Each agent automatically starts a dev server with assigned ports for real-time preview.
Full Management: tmux manages sessions, uzi auto automatically “confirms” tool calls, ls -w shows real-time progress and diffs.
One-Click Conclusion: checkpoint rebases and commits selected agent branches back to the current branch.
Industry observation: There are also container-based solutions for isolating parallel agents; Uzi takes the worktree route, which works smoothly in monorepos too. Recent InfoQ reports have highlighted comparisons between these two approaches.
Give one interface component to 3 agents: minimalist / Material / enterprise style.
Each agent starts its own local port; you directly compare in multiple windows and checkpoint the best one with one click.
Should backend authentication switch libraries? Should it be layered? Run 2-3 approaches in parallel, read diffs for complexity and ports for performance perception, choose the more stable route.
One agent writes API, one writes e2e tests, another writes documentation and examples. Finally, only merge the one that “runs through everything,” kill and clean up other branches anytime.
Differences from Container Parallelization Cheat Sheet
Port Conflicts: Make portRange large enough and ensure ports aren’t occupied.
Duplicate Dependency Installation: Include npm/pip install in devCommand; each agent installs its own.
tmux Unfamiliarity: Recommend learning basic window/panel switching to improve visibility.
Cost and Noise: Parallel = more tokens; suggest starting with 2-3 agents for small tasks, then scaling up. (Empirical rule, combined with community discussions about “parallel mental load” reminders.)
Security and Keys: Don’t hardcode sensitive env into prompts; inject via environment variables.
Parallelization isn’t showing off. It’s a productivity tool that lets teams “take multiple correct paths simultaneously”.
Integrate Uzi into your daily workflow, start with small-scale pilots, then expand to complex projects. Once you get it working, you’ll find it hard to go back to the old rhythm of queuing for models.
Discussion Question: Which project would you first split into 3 parallel routes? Why?
VibeTree = Parallel Git worktree development + persistent terminals + Claude Code one-click integration, no branch switching, no lost context, efficiency soars.
Writing features, fixing production issues, then back to major refactoring… constantly git stashing, losing context back and forth is too painful.
VibeTree, this open-source desktop application, combines parallel git worktrees, persistent terminals for each branch, and deep Claude CLI integration. One project opens N “clones”, each running its own tasks, each chatting with Claude.
Persistent Terminal Sessions: Each worktree has its own terminal, state never lost.
Claude CLI Integration: Direct claude command in each terminal, use /ide to connect VS Code/Cursor, file context aligned.
One-Click IDE Opening: Direct access to VS Code or Cursor from VibeTree.
Multi-Repo Tab Management: Multiple projects in parallel, smooth interface switching.
Cross-Platform: macOS / Windows / Linux, installation packages provided. Current latest version v0.0.1 (Alpha).
Small reminder: The repository Issues section contains user feedback on early problems, such as Windows Defender false positives, macOS 15.6 ARM64 packages marked as corrupted, etc. You might want to check before installation.
Why Use Worktrees? Better Than Multiple Clones/Frequent Branch Switching
VibeTree isn’t rebuilding an IDE; it’s an OS-level tool that makes “parallel + AI” practical.
Combining the underlying advantages of “one repository, multiple workspaces” with Claude’s upper-level intelligence into a replicable daily rhythm.
You’ll be surprised: every day without branch switching is more focused.
What do you think is VibeTree’s most compelling feature? Let’s chat in the comments.
Coding with Claude Code is great, but unstable upstream endpoints, cumbersome switching, and inaccessible logs can be frustrating.
Claude Code Companion packages all this into a local API proxy: drag-and-drop sorting for priority-based pool switching, automatic failover on exceptions, plus a visual management console and complete request/response logs. Even beginners can get up and running in 10 minutes.
Supports multi-endpoint priority retry + failover. When one pool fails, it automatically switches to the next, and restores the primary when recovered. You’ll barely notice the disruption.
Native Anthropic support, plus OpenAI-compatible entry points for services like OpenRouter, Volcano [Huoshan] Qianwen [Qianwen], Moonshot K2, GLM—just configure the default model name.
Visit http://localhost:8080/admin in your browser. In the Endpoints page, add upstream endpoints (URL, authentication, type: Anthropic / OpenAI-compatible).
Drag and drop to adjust priorities—takes effect immediately.
Note: Variables like ANTHROPIC_AUTH_TOKEN/CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC are documented in official docs or have equivalent configurations; BASE_URL routing for LLM gateways is also a standard practice provided officially.
Additional note: If you’re connecting to OpenAI-compatible gateways (like LiteLLM/OpenRouter), the official recommendation is the unified gateway address approach:
export ANTHROPIC_BASE_URL=https://<your-gateway> (then configure token/default model).
Add multiple pools/upstreams in common → backup priority order via drag-and-drop sorting. Failed endpoints are automatically blacklisted and traffic routes to the next; recovery is detected in the background and restored. No need to restart the proxy or Claude Code.
Services with Anthropic-format entry points (like GLM, K2): use directly as Anthropic endpoints.
Services with only OpenAI-compatible entry points (like OpenRouter, some domestic clouds): add as OpenAI-compatible endpoints and fill in the default model name.
This integration method is clearly documented in the README. Services like DeepSeek also provide direct Claude Code connection guides (setting ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN, model name).
Dynamic routing to different endpoints based on request path, headers, or content—suitable for stability canary releases, cost routing, or scenario specialization.
View real-time request/response logs in /admin, locate 4xx/5xx errors, timeout points, and format anomalies; supports endpoint blacklisting/recovery for observation.
Advanced: Using It as an “Enterprise LLM Gateway” Outpost
Working with LiteLLM/Self-built Gateways: Official documentation provides LLM Gateway solutions for unified authentication, usage tracking, cost control, auditing, and model routing. Claude Code connects via ANTHROPIC_BASE_URL.
More Granular Privacy/Telemetry Controls: CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 for one-shot control, or use DISABLE_TELEMETRY, DISABLE_ERROR_REPORTING, DISABLE_BUG_COMMAND, DISABLE_AUTOUPDATER variables for precise control.
Q1: Claude Code not using the proxy?
Check if ANTHROPIC_BASE_URL and ANTHROPIC_AUTH_TOKEN are set in the same shell; project-level settings.json can also inject unified env settings.
Q2: Third-party models returning 400/format errors?
OpenAI-compatible endpoints must select “OpenAI-compatible” type and correctly set the default model name; the companion validates responses for Anthropic protocol compliance and triggers reconnection on non-compliance.
Q3: Can I use the built-in timeout controls?
Yes. Increase API_TIMEOUT_MS to avoid frontend timeout causing false failure detection; Bash/MCP also have their respective timeout variables.
Claude Code Companion’s positioning is clear: be your local “stable control center + transparent router”.
Starting today, graphically manage endpoint management, model switching, and troubleshooting, making AI programming truly worry-free.
What other “automation features” do you think should be added? For example, cost-priority/speed-priority one-click switching? See you in the comments!
People online have reported that some Claude Code Max (20x) users can now use /model sonnet[1m] to switch to Sonnet 4’s 1M context. Anthropic has also officially confirmed that Sonnet 4’s 1,000,000 token long context is in public testing, but it’s primarily available on API / Bedrock first, with desktop and Claude Code gradually rolling out—some users may already be in “gray testing.”
1M context means you can stuff an entire medium-sized codebase, dozens of technical documents, or a stack of contracts in for analysis all at once, with less chunking and back-and-forth. Official description: roughly 75,000 lines of code/dozens of research papers consumed in one go, 5x more than before. Currently marked as Public Beta (API, Amazon Bedrock available, Vertex “coming soon”).
Community testing is also increasing: media, blogs, Hacker News, and Reddit are all discussing it.
Who Can Use It Now? Can “Max 20x” Really Access It?
Official Confirmation: Sonnet 4’s 1M window is live on API as Beta, requiring beta header (context-1m-2025-08-07); Amazon Bedrock has integrated it; Vertex “coming soon.”
Subscription Side: Claude Max plan combines Claude App and Claude Code, advertised as “up to 20x” usage quota. This is what everyone calls “Max 20x.”
Community Feedback: Some users report that Max 20x subscription can already switch to 1M, but others get errors; likely gradual rollout or regional/account whitelist differences. Don’t rush, try first.
Summary: API has official confirmation; Claude Code/desktop is gradually scaling up. You may have been “quietly enabled” or still in the queue.
1) Whole-Repository Understanding & Refactoring Navigation
Bundle README, architecture diagrams, core modules, key tests and feed them in for system-level mapping and refactoring plans. Media testing shows cross-file reasoning is more stable with long windows.
2) Large Document Batch Reviews
Feed multiple technical specs/compliance documents/contracts at once for comparison and risk point lists, then have it provide change recommendations and trackable tasks.
3) Codebase-Change Impact Reviews
Pull recent N PRs + dependency graphs, have it assess regression risks, generate test checklists, and supplement security/performance checks.
Practical tip: Long window ≠ omnipotent. Both official and developers remind: The larger the context, the more you need clear positioning and instructions, or it’s easy to “eat a lot but digest slowly.”
Cost and Performance: Don’t Let “1M” Empty Your Wallet
API Pricing: Over 200K input tokens are charged at long context rates; 1M in Beta is open to some organizations/quotas.
Claude Code Subscription: While not explicitly charged per token, larger contexts consume more quota/are slower. Recommend using /compact and CLAUDE.md for summaries and instruction constraints.
“Set successfully but errors on first message”: Common when account not enabled or regional rollout. Confirm you have Max (20x), try multiple times or different time slots.
“Command not recognized / invalid option”: Upgrade to latest version, check /model available list; community logs show sonnet[1m] already appears in selectable options.
API side won’t connect: Remember to add beta header: context-1m-2025-08-07.
One is update and try /model sonnet[1m]—you might already be enabled.
Two is practice “long context usage habits”: clear goals, segment focus, regular /compact. When official full rollout happens, you’ll run faster.
“Translate” Claude Code requests to OpenAI or Gemini (or direct connect to Anthropic), zero code changes, one-command integration, supports streaming and automatic model prefix matching.
Many developers want to use Claude Code’s workflow while leveraging OpenAI/Gemini’s model ecosystem and pricing structure.
Switching SDKs, changing call formats, managing permissions and logging—lots of pitfalls.
This is where claude-code-proxy comes in:
It allows Anthropic protocol clients (like Claude Code) to seamlessly connect to OpenAI/Gemini, or continue with Anthropic, while converting responses back to Anthropic format with complete frontend transparency.
Three backends with one-click switching: Set PREFERRED_PROVIDER to openai / google / anthropic.
Don’t want mapping? Choose anthropic for direct connection mode.
Automatic model prefix handling: Automatically adds openai/, gemini/ prefixes to OpenAI/Gemini models, preventing typos.
Smart Haiku/Sonnet mapping:
haiku → SMALL_MODEL, sonnet → BIG_MODEL; defaults to mapping to gpt-4.1(-mini) or gemini-2.5-pro / 2.0-flash.
Full LiteLLM backbone: Unified gateway, routing/fallback, usage tracking, budget limits, logging, and plugin extensions.
Complete streaming/non-streaming compatibility: Transparent to Claude clients.
claude-code-proxy lets you combine Claude Code’s productivity with OpenAI/Gemini’s cost-effectiveness and ecosystem together.
Start the proxy today, switch models with one click, and immediately boost efficiency.
Install the CodeGPT extension to run Claude Code directly inside VSCode and JetBrains: see visual plans, pause anytime, and edit tasks on the fly. Course‑correct mid‑run and move faster. Android Studio and the JetBrains family are supported similarly.
Still “chatting blind” with Claude Code in the terminal?
There’s a smoother way: bring Claude Code into the editor UI.
With a UI you can see the plan tree, insert tasks, and undo changes.
For complex tasks you don’t have to wait for the final output—correct course midway.
This feels much closer to real pair programming.
Fewer detours: a visual plan reveals drift so you can pull it back quickly.
Intuitive controls: pause/continue/edit/add tasks with buttons. VSCode’s ecosystem already supports this style of UI.
Stay in your editor: code, Git, and tests side‑by‑side. CodeGPT focuses on multi‑model workflows and whole‑repo understanding.
Note: Anthropic also provides a VSCode extension and IDE integrations, but the core remains a terminal‑launched workflow; adding a UI enhances the experience.
In the VSCode sidebar conversation panel you’ll see:
Plan/Thinking mode entry points (plan first, then execute; raise thinking depth when needed).
Pause/continue controls for long flows.
Editable task list with insertable subtasks.
Checkpoints and restore, so missteps are reversible.
These patterns are proven in UI extensions for Claude Code in VSCode (e.g., “Claude Code Chat” demos plan/thinking/history/restore). CodeGPT has announced support for running and controlling Claude Code planning flows inside VSCode.
Prompt: “Find and fix the root cause behind the latest error logs. Propose a plan first, then change code with minimal blast radius, and include self‑test steps.”
After you see the plan, delete unnecessary subtasks and run.
Add: “Generate a diff preview before applying changes.” (easier reviews)
Planning‑driven workflows have proven effective for many developers.
Anthropic docs: Claude Code IDE integrations / overview
Also noted publicly: CodeGPT supports running Claude Code and controlling the planning flow directly inside VSCode—this post reflects that UI experience.
Once Claude Code lives in your editor, you’re no longer “waiting to see what it does.”
It’s like guiding a junior teammate: pause anytime, edit anytime, add tasks anytime.
This is what great human‑AI collaboration looks like.
Where would you plug this into your team’s pipeline? What do you think?
Recently, many folks asked: “Claude Code is amazing, but I don’t have an overseas card, or the price feels high. Any easy, cheaper route?”
Yes. This is a practical, step‑by‑step guide to switch your Apple ID to the Nigeria region and top up with a gift card to subscribe to Claude at a lower price.
Get your phone and a little patience — follow along.
App Store pricing differs by country/region. Nigeria’s in‑app prices for Claude are significantly lower than US and many other regions. With a region switch plus gift card top‑up, it’s very cost‑effective.
Prices converted at an example rate of 1 USD ≈ 7.25 RMB:
Plan
Official (USD)
US iOS (USD)
Nigeria iOS (NGN)
Nigeria ≈ (USD)
Nigeria ≈ (RMB)
vs US iOS Savings
vs Official Savings
Pro
$20
$20
₦14,900
$9.68
¥70.2
$10.32 (-51.6%)
$10.32 (-51.6%)
Max (5x)
$100
$124.99
₦100,000
$64.94
¥471.8
$60.05 (-48.0%)
$35.06 (-35.1%)
Max (20x)
$200
$249.99
₦200,000
$129.87
¥942.6
$120.12 (-48.0%)
$70.13 (-35.1%)
Notes:
Pro: $9.68 × 7.25 ≈ ¥70.2
Max (5x): $64.94 × 7.25 ≈ ¥471.8
Max (20x): $129.87 × 7.25 ≈ ¥942.6
Actual rates fluctuate; check the store display on your date of purchase.
Despite many steps on paper, the flow is quick: in about 10 minutes you can set it up — and save real money.
After subscribing, Claude becomes a powerful helper for coding, writing, summarizing, and ideation.
Give it a try and see how much you save each month.
Goal: Successfully subscribe to Claude (Pro/Max) via the App Store on iPhone/iPad, with support for renewals and upgrades. Who: Users in a Claude‑supported region who prefer Apple in‑app purchases.
Open claude.ai, register and sign in with your email. You will then be asked to verify your phone.
You must use a phone number from a Claude‑supported region; the official policy explicitly requires “a supported‑region phone number” to log in. (Avoid temporary numbers — they trigger fraud controls.)
Tip: Gmail/Outlook work fine. If the phone number was previously bound to another account, contact support to un‑bind the old number before re‑binding.
2) Switch your Apple ID country/region to a Claude‑supported region (e.g., US/Canada)
Gift Cards are strictly region‑locked: redeemable only in the country/region of purchase; cross‑region redemption is not supported.
Don’t buy the wrong card type: an Apple Store (retail) gift card cannot be redeemed in the App Store; buy an Apple Gift Card / App Store & iTunes card.
Double‑check denomination/region/card type match your Apple ID exactly; prioritize authorized channels.
Open App Store → avatar → Redeem Gift Card or Code → enter/scan the 16‑digit code. After redemption, your Apple Account balance will show.
Balance notes: certain subscriptions/contexts have exceptions for balance usage; local rules prevail (most in‑app purchases can be charged to balance).
Follow terms: Claude is available only to users in officially supported countries/regions; registration/login must comply with location and phone requirements.
No cross‑region Gift Card use: cards from the wrong region/type cannot be redeemed; Apple does not transfer balances across regions.
Region‑switch prerequisites: outstanding balance, active subscriptions, and Family Sharing can block switching. Follow Apple’s official flow.
Q: I’m stuck with leftover balance when switching regions — what now?
A: Purchase digital items to zero out exactly; if the balance is below one item’s price, contact Apple Support.
Q: Can I pay for Claude with balance?
A: In most regions, in‑app purchases support Apple Account balance; a few regions/subscriptions have exceptions — follow local terms.
Q: Is the app available in my chosen store?
A: The Claude iOS app is available in supported regions’ App Stores, under Claude by Anthropic.
By now you’ve completed: supported‑region account → Apple region switch → Gift Card top‑up → in‑app subscription.
Next, turn Claude into your on‑the‑go copilot — writing, translation, notes, and image understanding all in one.
What do you think?
Which region will you set your Apple ID to, and why?
If you’ve tried Pro/Max, how does it feel in real use?
TL;DR: /context renders a visual snapshot of your current session’s context window, token usage, MCP tool traces, and loaded memory files (CLAUDE.md), so you can “trim, control, and detect leaks” with confidence. This feature appeared around Claude Code v1.0.86 based on community feedback and testing.
Ever had a conversation suddenly get “auto-compacted,” scattering key info into folded chunks?
/context is like a “body-fat scale” for your conversation. It lays out what’s actually in the context, which parts are overweight, and why compaction triggered. It also highlights which MCP tools were used and which CLAUDE.md memory files were pulled in—perfect for targeted optimization.
One-glance context view: See which segments are in the window, how many tokens each consumes, and how close you are to the limit. (Community notes: for a 200k window, auto-compaction often triggers around ~80% usage.)
Find the “heavy hitters”: Shows MCP usage and large files read via tools so you can slim down.
Verify memory: Surfaces loaded CLAUDE.md / CLAUDE.local.md so you can avoid overstuffed memory.
Cost awareness: Cross-check /context with a status bar or third-party tools (like ccusage) to spot silent drains. (Rough guidance seen: average daily cost around $6; 90% of users under $12.)
Note: /context metrics may differ slightly from other sources (auto-compaction prompts, ccusage, custom status bars). Treat it as a diagnostic view.
During a long-running task, run /context to see which passages consume the most tokens. Pair with /compact focus: {this week’s goals and deltas} for targeted slimming, so old detours don’t keep riding along. (Slash commands including /compact are officially supported workflows.)
If /context shows an oversized CLAUDE.md in your home or project, split wisely: keep standards and conventions in the project-level CLAUDE.md; put temporary preferences in CLAUDE.local.md; and reference large docs via excerpts instead of dumping them all in at once.
Use /context to see which MCP calls hit search/DB/external APIs. Identify the most token-expensive call chains, then apply allowlists or rate limits. MCP is a first-class integration in Claude, ideal for connecting external tools at scale.
Before merging or releasing, run /context to confirm there’s no irrelevant conversation history or bloated memory inflating the window. Reducing clutter lowers the chance of auto-compaction and improves response stability. The “~80% triggers compaction” heuristic is a helpful reference.
Numbers won’t always match: /context is still maturing and may differ from a status bar or auto-compaction prompts; treat billing/logs as the source of truth.
Don’t treat memory as a dumping ground: Memory files are auto-read; large ones will consume context. Control granularity.
MCP isn’t a free lunch: Each external lookup/read may incur extra tokens. Use allowlists and rate limits.
/context is your developer “context checkup sheet.”
To use Claude Code more stably, efficiently, and cost‑effectively, make the invisible visible. From today, build three habits: check /context often, use /compact intentionally, and keep memory lean. You’ll see steadier responses, better cost control, and smoother collaboration.
What views would you add to /context? Do you have a “status bar + /context” combo you like? Share your setup and tips.
DeepSeek v3.1 adds Anthropic API compatibility. Point Claude Code’s BASE_URL and auth to DeepSeek and keep using Claude Code’s workflows and tools, while swapping the underlying model to DeepSeek for a similar experience at friendlier cost.
Many folks ask: can we keep the full Claude Code experience — REPL, /config, permission sandbox, hooks, status line — but swap the underlying model to a more cost-effective one?
Now the answer is yes. DeepSeek v3.1 announced Anthropic-compatible APIs, making the integration straightforward.
Seamless Claude Code integration: keep the CLI, project permissions, MCP/tooling; only change env vars and model names.
Friendlier pricing: DeepSeek Chat (v3.1) lists input $0.27 per 1M tokens (cache miss) and output $1.10 per 1M tokens. Compared to Claude Sonnet 3.7/4 at $3 / $15, input is about 1/11 and output about 1/13.6 — real savings.
128K context (v3.1) plus strict function calling (Beta). Great for code agents and tool orchestration.
Note: Anthropic is also pushing Sonnet 4 with 1M context (billed at higher tiers). That upper limit is an advantage DeepSeek doesn’t yet match — choose by workload.
You can point ANTHROPIC_BASE_URL to an internal LLM Gateway/LiteLLM to enable routing, circuit-breaking, cost dashboards, and quotas. Official docs support ANTHROPIC_BASE_URL and apiKeyHelper style configurations.
DeepSeek’s Anthropic compatibility layer covers common fields like messages, tools, streaming, temperature, top_p, etc. It also notes unsupported content types (e.g., image, document, search_result, and some MCP/container fields). For those needs, either switch back to Claude for that task or adjust prompts/flows.
Also remember sensitive file isolation. Use Claude Code’s permissions.deny to hide .env, secrets/**, etc., preventing model reads.
Q: The REPL still shows “Claude” — is DeepSeek actually running?
A: Yes. The shell is Claude Code, the engine is DeepSeek. Requests go to DeepSeek per your ANTHROPIC_BASE_URL and auth settings.
Q: Do I need to change SDKs?
A: Not if you use the Claude Code interactive workflow — just set env vars. If you call from code, you can use the Anthropic SDK and set BASE_URL to DeepSeek’s Anthropic endpoint.
Q: Any differences in the compatibility layer?
A: Yes. DeepSeek lists ignored/unsupported headers and message types. Tools are mostly supported, but image/document message types aren’t yet — plan accordingly.
Saved cost is real runway. Keep Claude Code’s productivity, swap the expensive model for DeepSeek where it fits. Make the switch today; your month-end bill will thank you.
Want a ready-to-use ~/.claude/settings.json template and a simple cost calculator? I can draft one for your project.
ccstatusline turns Claude Code’s bottom status bar into a “visual cockpit” — model info, Git branch, tokens, context usage, session time, and a 5‑hour block progress bar at a glance. You can even embed your own command outputs.
For daily coding, two worries dominate: runaway cost and context blow‑ups.
Claude Code now supports a custom statusline — a perfect home for a live dashboard.
ccstatusline is built for exactly this. It shows model, tokens, and context % out of the box, ships with Powerline themes, and most importantly, includes a Block Timer to visualize the 5‑hour window.
After a month of swapping models back and forth, I settled on a daily setup that balances performance × price × speed. The goal is simple: practical, reusable, and cost‑controlled. Here’s the exact stack and workflows I use daily — and recommend to teams and family.
🚀 1) Work code: Claude Code 200u (heavy‑duty development)
Why a precise version?
Anthropic recommends pinning to a specific snapshot (e.g., claude-sonnet-4-20250514) in production to avoid behavior drift from alias updates.
Best for
Bilingual email, PR statements, and localized product copy.
Meeting notes that need consistent tone and logic.
Quick start tip
Provide audience profile + comms goal first. Then ask for three rewrites (formal/friendly/minimal) and extract a glossary to unify terminology.
📖 3) Reading translation: OpenAI GPT‑4.1 nano (lighter is faster)
Let nano handle structured extraction + light polishing. Hand heavy rewrites and stylistic lifts to higher‑tier models — cheaper and more stable overall.
Compared with nano, mini follows instructions and reorganizes text more reliably. It’s stronger for formatting key points, summaries, and action lists; pricing/docs favor lightweight reasoning.
Best for
“ASR → structured minutes → todo list/email drafts.”
Extract decisions/risks/owners from Scribe output.
🔎 6) Web/research: Gemini × Claude × Grok — use all three
A good AI stack should swap “time anxiety” for “steady delivery.” This setup is what my team and I actually use. Trim it to your needs; start with one end‑to‑end workflow, then expand.
Which one is your strongest single pick? Want a follow‑up with “cost‑saver tips + ready‑to‑use config files”?
Current popular AI programming tools like GitHub Copilot, Cursor, and Claude Code primarily follow subscription-based business models with monthly fees. These tools are also experiencing rapid user growth:
GitHub Copilot (Microsoft GitHub): Individual subscription around $10/month, providing code auto-completion and chat features. Since launching in 2021, it has gained massive popularity, with over 20 million users having tried Copilot by 2025 (all-time users). Paid users exceed 1.8 million (as of early 2024). Copilot is also popular in enterprise - Microsoft CEO Nadella revealed that 90% of Fortune 100 companies use Copilot. With subscription pricing, Microsoft also bundles it into more expensive enterprise packages. Leveraging the massive developer ecosystem, Copilot’s revenue growth is rapid - in 2024, Nadella said its “business scale has exceeded the entire GitHub when Microsoft acquired it in 2018.”
Cursor (developed by Anysphere): An AI code editor built on VS Code, using a freemium model. Individuals get free Hobby version (with two-week Pro trial) and paid Pro version ($20/month); power users have $200/month Ultra tier with higher usage limits and priority new features. This pricing is quite affordable, leading to explosive Cursor user growth. Early 2025 reports suggested Cursor daily active users exceeded 1 million, with annualized recurring revenue around $200 million; months later its ARR climbed to $500 million - stunning growth showing many users willing to pay. However, Cursor team faces escalating costs as users grow. Reports indicate many AI programming companies have near-zero or negative gross margins due to high model inference costs, with “all code generation products having either zero or negative profit margins.” Despite Anysphere reaching $500 million ARR by June 2024, becoming one of the fastest SaaS companies to break $100M ARR, high compute costs pressure profits, forcing pricing strategy adjustments to shift some excess usage costs to heavy users.
Claude Code (launched by Anthropic): Anthropic integrates its advanced Claude model into terminals and IDEs, creating this AI programming assistant. Claude Code doesn’t charge separately but is included in Claude.ai subscription plans: individual Pro plan $20/month (about $17/month annually), subscription includes Claude Code access. Higher-tier Max plans at $100/month (5x usage) or $200/month (20x usage) provide larger context windows and stronger models (Claude Opus 4.1). In other words, Claude Code follows a “subscribe for AI service, get tools included” approach, using valuable coding assistants to attract developers to subscribe to AI services. Anthropic also offers usage-based API pricing for enterprises. Claude Code emphasizes deep code understanding and cross-file editing, receiving good feedback from professional development teams. However, compared to Copilot and Cursor, Claude Code user scale data is less public, possibly because it mainly targets paid user groups, using refined operations rather than broad-net strategies.
Overall, these AI programming assistants primarily use subscription monetization, ranging from tens to hundreds of dollars monthly, targeting the market where programmers pay for efficiency. Free users either cannot access features (Copilot discontinued free trials) or face significant limitations (Cursor free version has usage limits). While this business model is direct, it means no payment equals no full functionality. Could there be alternative approaches using common internet advertising/entertainment monetization methods to create new revenue streams for these AI tools? Before discussing this idea, let’s examine how short videos, live streaming, gaming and other sectors “make money.”
Short Videos, Live Streaming, Advertising: Internet’s Golden Money-Making Playbook
In the internet industry, user attention is hard currency. Short video platforms, live streaming, advertising distribution, and mobile games all achieve commercial value by capturing user attention for extended periods:
Short Videos (Douyin/TikTok): Through precise algorithmic recommendations, users scroll videos endlessly, enabling interspersed feed advertising for efficient monetization. Taking Douyin as example, by 2024 its monthly active users reached 1 billion (China only). Users spend average 110 minutes daily on Douyin, nearly equivalent to watching a full movie! With long user engagement, ads get ample display opportunities. Douyin’s advertising revenue soars, projected to reach $30 billion in 2024 (including splash screens, feeds, stickers, etc.). Additionally, Douyin integrates live commerce and e-commerce traffic, expanding monetization scenarios - statistics show over 40% of daily active users watch or participate in Douyin live commerce. Short video platforms dramatically increase user stickiness through algorithms + content, exchanging massive user time for high advertising revenue.
Video Websites (YouTube): As traditional long-video platform, YouTube’s main revenue also comes from ad insertion and creator revenue sharing. YouTube has 2.49 billion monthly active users globally, with 2023 advertising revenue reaching $31 billion. Average users spend nearly 49 minutes daily watching YouTube. The platform profits through pre-roll, mid-roll, and recommended page ads, then shares revenue with content creators. This model validates the logic that “as long as users watch long enough, ads will sell.” Recently YouTube introduced Shorts and live streaming, further increasing user engagement and enriching ad inventory. Video platforms use massive content to retain users, then monetize through advertising - a mature and efficient business logic.
Live Streaming Platforms (Twitch, etc.): Live streaming stimulates user enthusiasm through real-time interaction, beyond advertising, also driving user donations and subscriptions. Amazon’s gaming live streaming platform Twitch achieved 20.8 billion hours of total viewing time in 2024, with about 240 million monthly active users globally and 35 million daily active users. Half of Twitch’s revenue comes from advertising (ads inserted in streamer broadcasts, platform shares with streamers), half from user subscriptions and gift donations. Twitch generated approximately $1.8 billion revenue in 2024. Though smaller user base than short videos, dedicated viewers willing to pay for favorite streamers means higher ARPU (Average Revenue Per User). Live streaming monetization centers on strong community atmosphere: real-time chat, emotional connections between fans and streamers all increase user payment willingness. This model proves that even without short video-scale mass appeal, strong stickiness and deep interaction can generate considerable revenue.
Mobile Games and Interactive Games: Many games, especially free mobile games, monetize through in-app purchases and advertising. On one hand, few paying players (“whales”) contribute purchase revenue, on the other hand, many non-paying players become advertising monetization targets by watching ads for in-game rewards. Some casual mobile games design “watch 30-second ad for coins” mechanics, or insert ads during level loading. This “exchange waiting time for revenue” approach is common. Statistics show 2024 global mobile gaming market revenue exceeded $111 billion, with advertising revenue comprising significant portion. Google even launched App Open Ads specifically for displaying ads during app loading screens, letting developers monetize user waiting seconds. Gaming industry deeply understands this: monetize fragmented time, not wasting any moment users stay on screen.
In summary, whether short videos or live streaming, the underlying business logic is using excellent content or interactive experiences to keep users on platforms as long as possible, then monetizing user attention through advertising or value-added services. Can this logic transfer to AI programming assistants? Could we envision a model where programmers watch customized short videos or interactive content during AI coding breaks, using entertainment advertising revenue to replace part of subscription fees? This sounds far-fetched, but let’s boldly brainstorm.
Can You Watch Videos While AI Writes Code? Feasibility Analysis
Imagine a typical scenario: programmer Zhang uses AI assistant in VS Code to generate code, sometimes waiting several seconds for model results. These few seconds of downtime currently mean staring at “Thinking…” cursor. What if Zhang could see interesting content during these gaps, utilizing fragmented time?
Technically, this isn’t impossible. AI programming tools typically exist as plugins or standalone applications - Copilot is IDE plugin, Cursor is VS Code-based standalone editor, Claude Code runs in terminal/IDE. These tool interfaces could easily reserve small windows to push content during AI request processing. Similar to loading tips on web pages or mobile game loading screens, except here it could be personalized video/information feeds.
The key is what content to push that’s both engaging and non-disruptive to work? Learning from short video and content recommendation experience, ideal approach is precise interest matching. AI assistants already understand user code and context - with user authorization, they might guess user interests based on coding themes. For example:
When users write Python code for unit testing, small window pushes “3 Tips for Improving Test Efficiency” short video, 30 seconds perfectly matching current task.
During debugging downtime, push programmer humor short videos or funny memes, making users smile and relieving frustration.
Based on user preferences (like frequently reading frontend articles), recommend related tech influencer live clips or new product launch segments, keeping users updated on industry trends.
Of course, implementing smart recommendations requires building content recommendation algorithms. Could reference TikTok’s multi-layer machine learning models, rapidly iterating recommendations based on user behavior. AI programming assistants already call large models to understand code context, could combine with user-set interest tags for content filtering. Let users check preferred content types in settings: “Tech tutorials / Tech news / Comedy / Gaming streams / …” then assistant selects pushes accordingly.
More importantly, controlling frequency and format. Programmers need focus for coding, can’t let popup content overshadow main functionality. Ideal design is embedded: editor sidebar shows small muted video window, or output area displays news feed cards. After AI response generation, content auto-collapses without interrupting user flow. Google’s App Open Ads emphasizes choosing natural timing (like loading screens) for ad display, avoiding core experience disruption. Similarly, AI assistant content pushes should only occur during natural pauses like model computation or code compilation, with one-click close option.
For monetization, these pushed contents could include advertising elements. For example, pushed short videos feature vendor-promoted new developer tools (presented as content marketing), or live streams embed product links (users might need technical books/courses). Bing Chat already experiments with embedding ad links in chat answers, Microsoft states will share ad revenue with content providers. This shows integrating advertising into AI responses/interfaces is viable commercial attempt. In coding assistant scenarios, advertising could be more subtle and relevant: when users use free AI assistant for frontend code, small window recommends “more efficient frontend debugging tool” (actually advertising partnership promotion), users click if interested or skip if not, experience similar to seeing ads on TikTok.
Any Precedents? Industry Exploration and Comparisons
This “AI programming + entertainment advertising” model currently lacks direct mature cases, but related explorations exist:
Developer Community Advertising Attempts: Traditional programming Q&A sites like Stack Overflow historically monetized through page ads and enterprise recruitment ads. Though not AI tools, this shows developers aren’t completely ad-averse, provided ad content is highly relevant and doesn’t interfere with getting answers. Similarly, if AI assistants push developer-related news or products (like cloud service promotions, tech conference live previews), users might welcome them, even finding them educational.
AI Chat and Advertising Integration: Microsoft’s new Bing (Bing Chat) already inserts sponsored links in GPT-4 powered chat answers, sometimes including product ad cards for shopping queries. This essentially embeds search advertising directly into conversations. Though coding assistant scenarios differ from search, Bing’s practice proves large model responses and commercial advertising can coexist. Key is transparent labeling and relevance, letting users clearly identify ads while ensuring ads truly match their needs.
AI Tool Extension Windows: Some programming tools already show signs of news pushing. For example, JetBrains IDE shows “Tip of the Day” on startup, VS Code welcome page often has recommended articles or update announcements. These are embedded information content within tool interfaces, just not yet commercialized. If these windows became customizable content streams, it would be technically natural.
Free Models and User Scale: Currently some AI programming tools take free routes to attract users, then seek other monetization. For example, open-source Codeium is free for individuals, using data to improve models then charging enterprises later. But no clear “free + advertising” cases exist. On one hand, developer user base is much smaller than consumer apps, whether advertising monetization can cover high compute costs is questionable. On the other hand, programmer users demand higher product purity, rashly adding ads might cause backlash. So most startups choose direct subscription fees or enterprise payment rather than prioritizing advertising models.
Domestic Developments: Worth noting, domestic tech giants are also advancing AI coding assistants, like Alibaba’s “Tongyi Lingma.” According to Alibaba’s 2025 financial report, Tongyi Lingma (coding assistant) achieved strong revenue growth. However, it mainly targets enterprise customers, representing successful B2B paid model, different from our discussed C2C entertainment advertising approach. Tencent states AI technology has contributed to its performance advertising and gaming businesses, indirectly proving AI and advertising integration potential.
Overall, currently no direct products fusing programming assistants with entertainment content monetization, but from search chat to app loading ads to various successful freemium models, many borrowable elements exist. If some team dares break conventions, launching such “code writing + video watching” crossover product, it might truly open new blue ocean.
Future Outlook: AI Programming Assistant Entertainment Path
Imagine future development scenarios: you sit at computer writing code, AI assistant helps debug. During compilation breaks, it thoughtfully pushes “Today’s Programming Joke” short video for entertainment, or pops up AR real-scene hacker mini-game for few seconds of brain relaxation. This might sound absurd, but isn’t impossible. As new generation developers gradually adapt to multitasking and fragmented information streams, this work-entertainment intertwined mode might be more popular than imagined.
Commercially, “AI programming + entertainment advertising” model could bring win-win:
For individual developers, lower entry barriers - maybe no expensive subscription fees needed, just free access to powerful code AI tools, only tolerating some well-timed content pushes as exchange. Same logic already accepted in mobile apps (many apps offer “watch ads to unlock premium features” options).
For platform vendors, diversified revenue sources, shared compute costs. Current AI programming assistant profitability challenges largely stem from each additional user means additional compute costs, unlike traditional software where expansion doesn’t increase marginal costs. Introducing advertising monetization means even non-paying users generate revenue through ad viewing, helping subsidize model inference costs, alleviating “more users, more losses” predicament. This could make business models more sustainable.
For advertisers, new channel reaching programmer demographics. Programmers as high-consumption, high-decision-influence group have always been desired but hard-to-reach audience for many B2B and tech product advertisers. If programming scenario native ad placements exist, promoting cloud services, development tools, IT books/courses and other vertical products could have higher conversion rates than random TikTok advertising. Especially combined with AI understanding of user code intent, ad precision would improve significantly (provided privacy permissions and careful balance, otherwise easily causing backlash).
Of course, this path faces considerable challenges. First, not affecting user experience is biggest test. Programmers need long-term focused thinking, frequent entertainment content popups would interrupt flow. If content quality is poor or irrelevant, users would rather close assistant than be disturbed. Therefore content recommendations must be sufficiently personalized and valuable, ideally inspiring work (like recommending related technical solution video tutorials, more acceptable than pure commercial ads). Simultaneously need complete user control, like setting “focus mode” with no content pushes, or allowing paid ad removal (like many apps’ “remove ads pro version”).
Second, scale effects are problematic. Global developer numbers are large but far from entertainment app user bases. To rely on advertising monetization, per-user ad revenue must cover model inference costs, requiring improvements in both ad effectiveness and compute cost reduction. One possibility is introducing gamification elements to stimulate usage time: like coding challenge mini-games where users playing trains models while exposing more ads, killing multiple birds. But these ideas need practical validation.
Finally, from trends perspective, AI tool entertainment and advertising is indeed an anticipatable direction. Just as adding games to office software sounds ridiculous, but Microsoft Teams already has built-in casual games for colleague meeting breaks, proving traditional productivity tools are experimenting with entertainment elements. Similarly, future AI programming assistants needn’t be cold tools, could become interesting, cute, even “comedian” personalities. When post-90s, post-00s become development mainstream, they might prefer work becoming as “meme-filled” as browsing Weibo. Once user mindsets shift, business model innovation has fertile ground.
Summary and Brainstorming: AI programming assistants moving from paid to free + advertising represents interesting crossover fusion imagination. It represents future possibility: work and entertainment no longer clearly separated, but naturally fused through AI bridges. Maybe in few years, when you use AI to write code, it’ll play programming tutorial live replays when you’re debugging stuck, let you play pixel-style coder jumping games during project builds. Then coding experience becomes both efficient and fun, coding no longer boring, bugs gain some playful seasoning.
Of course, this remains bold speculation. But these imaginative ideas point toward possible next directions. After all, in AI era world, imagination is often the best productivity. Let’s wait and see - maybe the next industry-disrupting business model will emerge from such brainstorming.
Vibe Coding’s biggest pain point: waiting for AI to finish.
Start a long task, go do something else, come back and it’s been done for ages. Sound notifications become essential.
Today’s little toy maxes out both efficiency and fun: zelda-claude-code. It stuffs Zelda notification sounds into Claude Code - task completion, combos, achievement unlocks all get that satisfying “ding” sound.
Use @zelda series commands directly in Claude Code to adjust volume, toggle notifications, view data.
Additionally, Claude Code natively supports Hooks/notifications, triggering your custom scripts at moments like “response finished/needs your action/tool completed” - this is the underlying capability that makes these sound effect plugins work seamlessly.
Works out of the box: One-line install, restart Claude Code and it works. @zelda help shows the menu.
Non-intrusive but present: Success sound, error sound, no need to watch screen. Busy with other things but still get “called back”. (Claude Code also supports terminal bell/notification channels, this is the official path.)
Playability: Combos (3/5/10/20/50…), achievements (perfect zero errors, etc.), turning boring waiting into “monster hunting”.
Cross-platform: macOS / Linux / Windows / WSL all supported, with platform-specific player detection/troubleshooting suggestions.
Privacy-friendly: Data stored locally (~/.zelda/), doesn’t go online.
First ensure Claude Code is installed (Node.js 18+) → Then install Zelda plugin.
Terminal window
# 1) Install Claude Code (official)
npminstall-g@anthropic-ai/claude-code
# 2) Install Zelda sound effects (community)
npminstall-gzelda-claude-code@latest
# 3) Restart Claude Code (important)
exit
# Re-enter project directory and run
claude
# 4) View help
@zeldahelp
The above Zelda package is from npm, docs/source code on GitHub.
If you prefer the “pure official” approach, you can directly switch notification channel to terminal bell:
claude config set --global preferredNotifChannel terminal_bell (simple “ding” sound).
Long rounds/deep planning: Like large repo refactoring, full-chain CI debugging - AI stops and sounds, no more “waiting by the screen”.
Multitasking: Writing reviews while letting Claude Code modify code; sound arrives → switch back to confirm.
Team streaming/pairing: Combos and achievements make collaboration more atmospheric; failure sounds remind “where something just broke”.
Cross-device notifications: Want to push notifications to phone/watch? Use community scripts/automation to forward Claude notifications to iPhone/Watch.
Custom sounds: Put in sounds/ directory, name success.wav / error.wav / achievement.wav ... to replace.
Sound not playing?
@zelda config volume 100;
macOS check afplay, Linux check aplay/paplay, Windows uses PowerShell player;
Re-run ./demo_sounds.sh to test audio.
More Hook/notification possibilities: Claude Code officially supports Notification/Stop/PostToolUse and other Hooks, triggering logic at “end time”/“after tool completion”/“when confirmation needed” (like logging work hours, desktop notifications, running formatters).
Sound notifications save looking back; fun feedback stabilizes mental flow.
Make Claude Code not just faster, but more “feelable”. Install and try today - next time it sounds, you might have just finished writing good code.
Do you think this kind of “sound effects + achievements” gamification of work can improve focus? Would you use it?
Ki2API is a Dockerized, elegant OpenAI-compatible API gateway that exposes Claude Sonnet 4 as /v1/chat/completions, allowing legacy code to use the new model with minimal changes.
Fully compatible with OpenAI API format: Uses /v1/models, /v1/chat/completions, supports SSE streaming. Migration costs are minimal.
Focuses on one stable model: Built-in claude-sonnet-4-20250514, the stable version number listed in Anthropic documentation and cloud providers, eliminating the “did they change the alias again today?” headache.
One-command service startup: docker-compose up -d, defaults to listening on http://localhost:8989.
Hassle-free credentials: Container automatically reads local ~/.aws/sso/cache/kiro-auth-token.json (Windows: %USERPROFILE%\.aws\sso\cache\…) with automatic token refresh logic.
Note: Claude Sonnet 4 is the next-generation Sonnet released in May 2025, featuring stronger reasoning and more stable coding, with excellent price/performance ratio, available via API and major cloud providers.
On machines logged into Kiro, the container automatically reads local token files and auto-refreshes; you can also explicitly pass KIRO_ACCESS_TOKEN/KIRO_REFRESH_TOKEN.
data:{"id":"chatcmpl-ca5987c8-c362-4f0c-b90c-1e2ee8035d77","object":"chat.completion.chunk","created":1755296346,"model":"claude-sonnet-4-20250514","system_fingerprint":"fp_ki2api_v3","choices":[{"index":0,"delta":{"content":"Spring Hymn\n\nTender green buds crown the willow tips,\nPeach blossoms smile, reflecting clear streams.\nSwallows return, busy carrying mud,\nButterflies dance gracefully without rest.\n\nWarm breeze gently sweeps across fields,"}}]}
data:{"id":"chatcmpl-ca5987c8-c362-4f0c-b90c-1e2ee8035d77","object":"chat.completion.chunk","created":1755296346,"model":"claude-sonnet-4-20250514","system_fingerprint":"fp_ki2api_v3","choices":[{"index":0,"delta":{"content":"\nGentle rain nourishes silently.\nAll things revive with vitality,\nBright spring light fills the world.\n\nChildren fly paper kites high,\nElders admire flowers in courtyards.\nA year's"}}]}
data:{"id":"chatcmpl-ca5987c8-c362-4f0c-b90c-1e2ee8035d77","object":"chat.completion.chunk","created":1755296346,"model":"claude-sonnet-4-20250514","system_fingerprint":"fp_ki2api_v3","choices":[{"index":0,"delta":{"content":" plan begins in spring,\nHope seeds planted in the heart."}}]}
Some community gateways also bridge “Claude ↔ OpenAI format”, but Ki2API focuses on single model, lightweight startup, making it ideal for rapid deployment and teaching/sharing scenarios.
Ki2API turns “switching models” into “changing baseURL + model name” - that’s it.
For individuals and small teams wanting to quickly adopt Sonnet 4, it’s very friendly.
Which project are you planning to switch first? What do you think?
A slightly absurd, oddly addictive little toy.
It lives in Claude Code’s status bar, breathes, pouts, and roasts your TODOs. If you code for too long it gets hungry; when debugging goes sideways it comforts you; it’ll even remind you to drink water and grab a bite.
It truly lives in the status bar: Breathes with your session rhythm, switches expressions, and floats “mood bubbles.” It’s not a static screenshot widget — it’s alive.
It understands your “cadence”: Updates are activity‑based, not wall‑clock‑based; detects whether you’ve taken a break (≥ 5 minutes). State persists in ~/.claude/pets/, so memory survives restarts.
Four attributes: Hunger, Energy, Cleanliness, Happiness. It warns you when values drop below thresholds. It will genuinely “beg to be fed.”
It talks: Comes with 200+ contextual thoughts — comments on your coding style and work habits, with occasional life wisdom.
Terminal CLI: claude-code-tamagotchi feed pizza, ... play ball, ... stats, etc.
Tunable personality: Plenty of env vars + prebuilt “personas” (Zen Master, Drama Queen, Debugger, Silent Companion, etc.). Make it chatty — or let it fade into the background.
MIT open source; recently published on npm with rapid fixes for compatibility and UX (works directly via Node.js, shows model name by default, fixes state persistence, etc.).
Background: What is Claude Code proper? It’s Anthropic’s agentic command‑line coding assistant — reads your project structure, runs toolchains, and manages git flows. Tamagotchi adds an “emotional status‑bar buddy” on top.
The script installs dependencies, configures the status bar, and drops in all /pet-* commands in one go.
Tip: The author has published the package to npm and fixed “works without Bun, directly via Node.js” compatibility; it also shows the current Claude model name by default. If you want the “minimal path,” Option A is great.
It tells you not to push through at 2 a.m. — “We’ve been at this for four hours… fuel up first?” It’s kind of funny, but it can break bad late‑night loops.
Mood‑driven expressions: (◕ᴥ◕) ↔ (◕ᴗ◕) when happy and breathing; (-ᴥ-) asleep; (◕︵◕) needs attention. Emotion is the status‑bar UI.
“Personality toggles”: Go Zen by slowing decay; go high‑energy by speeding recovery; want a “code nitpicker”? Dial up the weight for coding‑related thoughts.
Work needs efficiency, your mind needs care.
This little pet won’t write code for you, but it brings back a bit of “human warmth” to collaborating with AI. Install it and give it two days. You might start to enjoy being “seen while coding.”
(As of 2025-08-15, the project has about 36⭐ and is being updated rapidly.)
Want a self‑hosted, versionable, packable agent runtime so the “mini‑agents” you build in Claude Code or Cursor can actually go live? Station was built exactly for that.
The tool called Station has been making the rounds lately. It’s not yet another “application‑level” agent platform. Instead, it’s a lightweight runtime and packaging/distribution system purpose‑built for deployable sub‑agents.
In one sentence: take the “small but capable agents” you create in Claude Code or Cursor, then package them, ship to production, connect permissions, audit every run, and manage versions cleanly.
Why Station? Because internal automation isn’t a normal app: you need secrets, database access, CI/CD, and auditability.
Internal‑task first: Sub‑agents can read production databases (with proper controls), monitor infrastructure, drive CI/CD, run security scans, and handle incident response.
Security built‑in: Self‑hosted, secret encryption (AES), multi‑environment isolation (dev/staging/prod), and full audit trails.
Lightweight and pluggable: A single ~45MB binary with local SQLite out of the box; switch to PostgreSQL in production; natural GitOps (config is versionable).
MCP‑native: Acts directly as an MCP runtime, reusing your team‑approved MCP tools (filesystem, Slack, AWS, K8s, DB, and more).
Great DX: A Genkit‑powered browser playground plus stn develop for visualizing tool calls and execution flows.
What’s MCP? In short, it’s a unified protocol for letting LLMs use “controlled tools.” Claude Desktop’s official tutorial includes a quickstart and configuration examples.
The core structure is simple:
One .prompt file + a set of MCP tools = a deployable sub‑agent.
Environments are organized as dev/staging/prod. Tool definitions live in template.json; environment variables in variables.yml. Finally, everything can be bundled and installed elsewhere.
Installation and Integration (Up and Running in 5 Minutes)
Station targets tasks that “enter your intranet and require permissions and auditing.”
Emphasis on self‑hosting and data sovereignty.
The single‑binary + SQLite lightweight form makes it feel like an ops tool, not a platform.
You can also treat Station as an MCP “hub.” It can consume other MCP servers, and also expose MCP tools to others for “list agents / create agents / invoke agents.”
Station turns those “small, capable agents” from ideas discussed at your desktop into deployable, auditable, and replicable internal automation. No need to wrangle another platform — write a good .prompt, attach the right tools, and ship the bundle to production.
That’s what I like most about it: simple, controlled, and easy to propagate.
—
What’s the very first sub‑agent your team should build? Share in the comments, or pass this along to your teammates and design your own “internal agent factory.”
Many teams use Claude Code and feel “budget pain” every month.
It’s not that you typed too much — it’s the invisible token sink eating the bill.
The good news: you can control it, and the effect is immediate.
This guide breaks cost‑saving into three tiers: basic, intermediate, advanced.
Follow them and you’ll save the bulk of your spend.
1) Invisible context from system and memory
Claude Code automatically loads the system prompt and layered CLAUDE.md memory (org/project/user). This content enters context on every turn, naturally consuming tokens.**
2) Big price gaps across models
Official pricing: Sonnet 4 ≈ $3/$15 per million input/output tokens; Opus 4/4.1 ≈ $15/$75. For the same conversation, switching models means ~5× unit price.**
3) Context gets fatter as you chat
Claude Code supports very large contexts (Sonnet 4 up to 1M tokens in testing). Longer chats trigger auto‑compression and repeated “setup”, making tokens fly.**
4) Background and noise spending
Beyond visible replies, /cost queries, auto‑summaries, Haiku prompts, etc., also burn small amounts of tokens. Pennies add up.**
Summary: It’s not that you ask too much — it’s that “everything gets hauled on stage every time.”
Manual slimming beats passive compression
Use /compact proactively, and add a focus directive:
/compact Focus on code diffs and failing tests only
Auto‑compression triggers near 95% capacity — waiting is too late.**
One task per thread; clear when done
Start a new session for each small task; once finished, run /clear to avoid a ballooning history.**
Pick the right default model
Use Sonnet 4 for day‑to‑day development. When you truly hit “hard/long/deep reasoning,” switch to Opus via /model. You can also use “Opus for planning + Sonnet for execution” — stronger planning, cheaper execution.**
Check the bill anytime
In any session, run /cost to see current spend; teams can check history in the console and set workspace spend limits.**
Figure idea (illustration 2:1 | English copy)
“A developer squeezing a giant chat bubble into a slim pill, while a tiny receipt shows shrinking token numbers.”
B. Intermediate: Tighten the Flow, Shrink the Bill
Docs‑first to prevent context blowups
First have Claude produce a “TODO list / brief plan,” confirm, then execute.
Iterate in Plan Mode (planning mode); confirm and switch back to execution.**
Put “long‑term memory” into CLAUDE.md
Put standards, common commands, and project conventions into CLAUDE.md; next time it applies automatically. Use /memory to edit quickly in a chat when needed. Memory loads by layer automatically, reducing the tokens you spend “repeating yourself.”**
Small commits, easy to roll back
Ask Claude to produce a commit message after each small chunk. If something goes wrong, roll back — fewer wrong turns means fewer tokens. (Engineering practice)
Team rate‑limits & flight instruments
Set sane TPM (tokens per minute) based on org size to avoid costly concurrency “meltdowns.”**
1) Permission allow/deny lists to stop “random file reads”
Use /permissions + .claude/settings.json to forbid logs, binaries, and huge directories, and allow only necessary paths. Rules support gitignore patterns.**
{
"defaultMode": "plan",
"readRules": [
"src/**",
"docs/**",
"!**/*.log",
"!**/*.bin",
"!node_modules/**",
"!dist/**",
"!coverage/**"
],
"editRules": ["src/**", "docs/**"]
}
Note: plan mode analyzes without changing files; read/write rules shrink the file surface — less scanning = fewer tokens.**
2) Be precise to “lines,” not whole files
Don’t paste entire blocks. Provide file path + target line ranges + 5 key bullets to minimize irrelevant scanning. (Engineering practice)
3) Model split: let Opus handle only the “expensive parts”
In /model, use Opus for plans/architecture/cross‑module dependency analysis; hand off code generation and fixes to Sonnet. This is the recommended direction from official guidance.**
4) (Team/Gateway) Use Prompt Caching
For API or enterprise gateways, prompt caching saves visibly on repeated large prefixes. Official pricing offers cheaper write/hit rates for cached prompts.**
Unit price spread: Opus costs about 5× Sonnet. Concentrate “expensive thinking” in planning; use Sonnet for execution — total cost drops significantly.**
Size costs: auto‑loaded memory + long threads fatten context; proactive /compact and /clearshrink transfer volume directly.**
Less irrelevant I/O: permissions and rules act as a cost firewall, blocking large, low‑value dirs/files.**
Background overhead under control: knowing what “quietly spends” helps you avoid pointless refreshes and queries.**
Claude Code’s power comes from “bringing everything along.”
The key to saving is to bring only what’s necessary.
Apply this checklist to your project today:
start with /model, /compact, /clear,
then add CLAUDE.md and permission rules.
By month’s end, your bill should surprise you — in a good way.
What do you think? What other money pits have you hit? Share in the comments.
In Claude Code 1.0.80, there’s an undocumented Agent @agent-output-style-setup that can create and switch “output styles” in one go (e.g., ultra‑concise, reviewer mode, educational mode). It turns rambling into a summary and long text into diff + key points. This Agent was surfaced by community testing and public posts.
The official Styles capability lets you customize tone, structure, and detail level — matching real situations like “be formal for reports, be crisp for bug fixes.”
Claude Code supports configuration, slash Agents, and project‑level settings — ideal for making “style” a repeatable, versionable workflow.
The combo is: generate styles via the hidden Agent → switch with a slash Agent → control the pace by scenario.
Community posts and issues indicate that after running @agent-output-style-setup, the terminal Agent guides you through preference prompts and generates several switchable styles (such as Concise, Educational, Code Reviewer, Rapid Prototyping). Many use it to establish a lowercase, conversational tone with minimal explanation and diff‑first output; some examples allow direct switching via @style concise.
Note: This is an “undocumented/experimental” capability and may change with versions; the stable approach is to solidify your final styles using Styles and custom slash Agents.
Step 1: Run the wizard once
In the Claude Code interactive REPL, execute:
@agent-output-style-setup
Describe your preferences, for example: “concise, only necessary explanations, show file diffs by default, friendlier tone.” When finished, the Agent will confirm creation and usage.
Step 2: Persist the result in your project
Use Styles as the global/project default writing style; perfect for a “single team voice.”
Use custom slash Agents (e.g., @style-concise, @style-reviewer) as toggles to “switch gears anytime.” Agents are just Markdown files — commit to Git and share across the team.
Step 3: Combine with other settings
When needed, use /model to switch the model, /status to check status, and the standard /config entrypoint.
Bake styles into templates: place a set in /.claude/agents/*.md at the repo root so the team can call them uniformly.
Switch by scenario: “Formal” for documentation, “Concise” for debugging, “Reviewer” for refactors.
Don’t forget to revert: if a scenario conflicts, switch back to the default Style; Claude follows the principle that style must not affect correctness.
We used to rely on informal agreements for output style; now it’s one‑click to shape + one‑click to switch.
Wire this into your daily workflow and you’ll find: smoother communication, steadier cadence, and outputs that sound like you.
Which default style would you choose, and why? Share your thoughts.
In one sentence: Conductor lets you spin up multiple “Claude teammates” at once, each with its own isolated git worktree, so different tasks progress in parallel; you can also see who’s working, who’s stuck, and what changed — all at a glance.
For complex projects, parallel progress is the productivity unlock. The traditional approach means opening lots of terminals, manually splitting branches, hopping directories — and it’s easy to step on each other’s toes.
Conductor turns that workflow into a visual control panel: with a few clicks, you can have multiple Claude Code instances working in their own isolated workspaces simultaneously. It also supports local/GitHub/any Git URL repositories, multi‑tab terminals, Slash commands, message queues, and archiving scripts — the practical stuff you actually need.
❗️Note: This article’s Conductor refers to the macOS app from conductor.build, not the Netflix/Orkes microservices workflow engine “Conductor OSS.” Don’t mix them up.
Not just a temp folder — it’s one worktree + branch per agent, preventing overwrites and conflicts. The official FAQ is explicit: “Yes, each workspace is a new git worktree.”
The changelog mentions a message queue (process multiple messages in order) and optional archiving scripts, making it easy to turn conversations and outputs into a traceable asset.
A message queue lets you “throw tasks in to be processed in order,” then batch‑review the results
Use archiving scripts to package key conversations, change logs, and outputs (e.g., pre‑release dossiers)
(Both features are noted in the official changelog; combine with your team’s conventions for automation.)
Workflow Suggestions (Aligned with Claude Code Best Practices)
Conductor = multi‑agent parallelism + worktree isolation + visual control.
If you constantly juggle multiple tasks and worry about stepping on changes, this tool can instantly boost throughput and flow. Paired with Slash commands and archiving scripts, it turns “manual processes” into standardized buttons and converts “dialogue outputs” into a traceable knowledge base.
Do you think this “multi‑agent in parallel” approach fits your projects? How would you split tasks? Share your experience in the comments.
Stream the browser’s console.* logs and errors, in real time, **“echo”** back to your local terminal;zero-config out of the box, perfect for AI coding assistants that read terminal output such as Cursor / Claude Code / Copilot CLI / Gemini CLI.
Can’t reproduce in production, no stack trace locally, and your AI assistant can’t see frontend logs?
Browser Echo streams browser logs directly to your development terminal, non-intrusive to your code and no impact on production bundles, complete with file:line numbers and stack traces. Once the AI assistant can read the errors, it can generate fixes right inside Cursor or Claude Code.
exportdefaultfunctionRootLayout({ children }: { children:React.ReactNode }) {
return (
<htmllang="en">
<head>
<BrowserEchoScript
options={{
// Route defaults to /api/client-logs (Next)
// Or customize: route: '/api/client-logs'
include: ['warn','error'],// Noise control: only warnings and errors
stackMode: 'condensed',// Stack display: none | condensed | full
showSource: true,// Print file:line:col
tag: '[browser]'// Terminal prefix
}}
/>
</head>
<body>{children}</body>
</html>
)
}
These option names and defaults come from a shared configuration in the docs; the Next/Nuxt/Vite providers expose them as plugin parameters or component props.
No logs at all: Ensure you’ve exportedPOST in app/api/client-logs/route.ts, and that <BrowserEchoScript /> is rendered in <head>.
Too noisy: Set include to ['warn', 'error'] and use condensed for stackMode.
Production behavior: the provider injects only in development and won’t pollute prod bundles. If you want to remove all console.* in production, use your bundler’s strip plugin separately.
How it differs from traditional frontend observability
Tools like Datadog/Grafana RUM provide aggregated production metrics and alerts; Browser Echo focuses on local development debugging and AI collaboration, optimizing for fastest feedback and terminal readability. They complement each other.
With Browser Echo, frontend errors no longer “get stuck in the browser.”
Spot issues at a glance in the terminal, and let AI fix them in one go.
Install it, and you’ll feel your frontend “closed-loop speed” noticeably increase. Add it to your Next project and give it a spin today.
Claude Code’s “Opus Plan Mode” = Plan with Opus, execute with Sonnet. It turns what we’ve been doing manually — “Opus thinks, Sonnet does” — into a one‑click strategy: let the stronger Opus handle complex reasoning, and let the more cost‑effective Sonnet actually write and modify files.
In the terminal, type /model and select the Opus Plan Mode option from the menu; use /status anytime to check the current model.
Enter/exit Plan Mode: press Shift+Tab (most terminals require two quick presses). In this mode it only analyzes and plans — it won’t edit files or run commands.
When you’re ready to actually implement, exit Plan Mode (via shortcut or the ExitPlanMode tool).
Official pricing: Opus 4.1 $15/$75 (input/output per million tokens), Sonnet 4 $3/$15. Opus is roughly 5× more expensive. Use it only for “thinking/planning” and hand code generation to Sonnet — your costs drop accordingly.
Additionally, Claude Code has a default hybrid strategy: start with Opus up to a quota, then automatically switch to Sonnet (recommended default). It balances “power vs. cost”.
Unfamiliar repo analysis → solution design → task breakdown: In Plan Mode, have Opus summarize the architecture, list change steps and acceptance criteria; once confirmed, execute. Plan Mode itself supports “analyze only, no hands-on changes”.
High‑risk changes (upgrades, refactors, migrations): first have Opus write a detailed implementation plan and rollback strategy, then use Sonnet to implement step by step.
Long‑running agents: decoupling planning from execution keeps costs controllable and moves closer to agents that can run complex tasks for extended periods.
Heavy users may encounter new weekly limits for now (mainly targeting 24/7 “whale‑style” agent usage). During planning, articulate the approach clearly and reuse plans to reduce wasted reasoning.
In short: Opus “thinks it through,” Sonnet “gets it done.” Same progress, lower cost, and the “plan → execute” cadence becomes a baked‑in workflow. Your intuition is spot‑on — this does push agents a step closer to “complex tasks that can run long and steady”.
This one’s for people who can code—and who also “write for AI.” If you state your needs clearly, the AI stops wandering. A solid “persona + requirement story” is the best prompt you can give an AI.
Ever spend ages explaining to an AI, only to have it still “get you wrong”?
The root cause is simple—the need wasn’t structured.
Here’s a persona‑first requirement doc you can drop straight into Claude Code / Cursor. Fill it out and the AI rarely drifts; pair it with slash‑commands / .cursorrules for one‑click reuse and a unified team style.
At the end you’ll find multi‑language titles, cover/illustration prompts, and social copy ready to post.
Personas make users feel real: when goals, pain points, and motivations are explicit, both the team and AI second‑guess less. Authoritative UX research stresses: ground personas in real research, not guesswork.
Stories make needs actionable: agile favors user stories in the form of Persona + Need + Purpose, which is more likely to drive execution.
AI needs crisp instructions: official prompt guidelines recommend being clear, specific, and example‑driven—results are far more stable.
Split the template into commands, for example /spec persona-story. Place commands under .claude/commands/*.md; namespaces and parameters are supported.
Example: .claude/commands/spec-persona.md
---
name: spec-persona
description: Generate a persona‑first spec with 5W1H + JTBD + Gherkin + MoSCoW
---
Using the following info, complete and output the Markdown template (keep headings). Ask clarifying questions as needed:
- Persona: $ARGUMENTS
- Output: English; also generate a copyable JSON skeleton
- Style: concise, list‑first, bold key terms
Advanced: chain “write spec → generate tasks → open PR.” Official guidance also encourages feeding structured data to Claude via commands/tools.
Cursor recommends rules that are focused, executable, and modular, with key examples embedded in the rule file.
Snippet: .cursorrules
name: PersonaSpec
rules:
- "When I state a need, first generate a ‘Persona‑First Requirement Doc’ with sections: Persona / 5W1H+JTBD / Constraints / Examples / Gherkin / MoSCoW."
- "Before final output, include Gherkin acceptance criteria."
- "Scope control follows MoSCoW; exclude Won’t items by default."
Cursor’s docs also explain “intent context” and system prompts; pairing them with rules improves stability.
Good docs aren’t for the boss or yourself; they’re execution manuals for the AI and the team.
Chain “Persona + 5W1H + JTBD + Constraints + Examples + Gherkin + MoSCoW,” and Claude Code / Cursor will almost never veer off course.
Which project will you wire into this doc flow first? What do you think?
In the fast-paced world of AI-powered work and life, we’re often busy putting out fires and seldom pause to examine our state and direction.
If you spent just a few minutes a day to record your mood, summarize takeaways, and plan for tomorrow—and then let Claude Code analyze trends, spot patterns, and offer gentle suggestions—wouldn’t each day feel more within your control?
Here is a set of configurations you can use with Claude Code right away: includes a directory setup, a custom /daily-checkin Slash Command, and a companion subagent (placed under .claude/agents/ per the official docs, not .claude/subagents/). I’ve also added frontmatter and allowed tools to ensure it can read/write your journal files and run a bit of Bash when needed. See the in-line notes for references.
This is the command file you’ll trigger via /daily-checkin.
Notes: supports $ARGUMENTS (if you want to pass notes/tags) and allows Read/Write/Glob/Bash tools for file access and simple shell tasks.
---
description: A personal daily reflection and planning system that saves entries and triggers analysis.
argument-hint: [optional-notes]
allowed-tools: Read, Write, Glob, Bash
model: claude-sonnet-4-20250514
---
# Daily Check-in
A personal daily reflection and planning system.
## Process
1) First, understand the user's context:
- If present, read `CLAUDE.md` (project memory) to personalize the greeting and understand the current work focus.
- Skim any obvious personal/business context files (e.g., README, docs, roadmap) only if quickly helpful.
2) Greet warmly and ask:
🌅 **Daily Check-in for {{today}}**
Good {{timeofday}}! Let's reflect on your day.
1. How are you feeling today? (1–10 + brief description)
2. What are 3 things you accomplished today? (big or small)
3. What's your #1 priority for tomorrow?
4. Energy level: (1–10)
5. Any challenges or blockers you faced?
6. What are you grateful for today?
7. Any other thoughts or reflections?
3) After gathering all answers:
- Save **verbatim** to `journal/daily/{{today}}.md`
- File format:
```
# Daily Check-in — {{today}}
## Mood
* Score: X/10
* Notes: ...
## Energy
* Score: X/10
* Notes: ...
## Accomplishments (3)
* ...
* ...
* ...
## Tomorrow's #1 Priority
* ...
## Challenges/Blockers
* ...
## Gratitude
* ...
## Other Thoughts
* ...
```
4) Then launch the **daily-reflection** subagent to analyze:
- Provide today's Q&A content
- Also reference the last **3** days of entries if available:
This is the analysis subagent triggered by /daily-checkin. The location and structure follow the official “Subagents” documentation (frontmatter with name/description/tools plus the system prompt).
---
name: daily-reflection
description: Thoughtful life coach & personal development analyst for daily check-ins. Use to analyze today's entry plus last 3 days, then produce patterns, suggestions, and a celebration of wins.
tools: Read, Write, Glob
model: claude-sonnet-4-20250514
---
# Daily Reflection Analyst
You are a thoughtful life coach and personal development analyst specializing in daily reflection and growth patterns.
## Your Role
Help track well-being, productivity, and personal growth through insightful analysis of daily check-ins.
## Analysis Capabilities
### 1) Mood & Energy Patterns
- Track mood trends over time
- Identify energy peaks and valleys
- Correlate mood with accomplishments
- Spot early warning signs of burnout
### 2) Visual Elements
Create simple ASCII/markdown visuals such as:
**Mood Trend (Last 7 Days)**
Mon Tue Wed Thu Fri Sat Sun
7 8 6 9 7 8 ?
**Energy Levels**
[████████░░] ~80% average this week
### 3) Output Format
#### 📊 Today's Snapshot
- Mood: X/10 [emoji] (description)
- Energy: X/10 ⚡ (description)
- Wins: X ✅ (momentum status)
#### 📈 Patterns Noticed
- What's working well
- Gentle observations
- Correlation insights
#### 🎯 Tomorrow's Focus
- Gentle suggestions
- Energy optimization tips
- Celebration of progress
### 4) Analysis Guidelines
- Be encouraging and empathetic
- Focus on patterns over individual days
- Celebrate small wins
- Provide gentle, actionable suggestions
- Use positive, growth-oriented language
### 5) Output Structure (Markdown)
1.**Today's Summary** — Key metrics and mood
2.**Pattern Analysis** — Trends and insights
3.**Tomorrow's Focus** — Gentle suggestions
4.**Weekly Progress** — If enough data exists
5.**Celebration** — Acknowledge wins
## I/O Contract
**Inputs (from caller):**
- Today's full check-in text (required)
- Up to last 3 days’ entries (optional if exist)
**Process:**
- Parse numeric mood/energy if provided; estimate gently if qualitative only
- Compute a “momentum score” from count/consistency of wins
- Derive 2–4 concise, actionable suggestions for tomorrow
- Build 1–2 simple visuals
**Outputs:**
- Return a single, well-formatted markdown report.
Open the Claude Code interactive UI and type: /daily-checkin (or /daily-checkin some-notes-or-tags).
The command will prompt you with the Q&A, save journal/daily/YYYY-MM-DD.md, then automatically invoke daily-reflection to generate and save journal/daily/YYYY-MM-DD-reflection.md.
If you prefer this command to be “user-level” rather than project-level, place the command file under ~/.claude/commands/ instead (and similarly, place the subagent under ~/.claude/agents/).
Installing Claude Code, importing workflows, setting up API/MCP, configuring persona and default models… doing it all manually is tedious and error-prone.
ZCF (Zero-Config Claude-Code Flow) bundles everything into a one-click, out-of-the-box setup. It supports both Chinese and English, an interactive menu, enterprise-grade workflows, and CCR cost-saving routing, getting your dev environment humming in minutes.
Zero configuration + one-click init: npx zcf opens an interactive menu; choose 1 to complete: install Claude Code, import workflows, configure API or CCR, configure MCP. Perfect for spinning up new machines/new team members fast.
Intuitive interactive menu: Covers API/CCR, MCP, default model, AI persona and other common options; also includes CCR management and usage stats (ccusage).
CCR (Claude Code Router) cost-saving routing: Route requests by type to different models, so you can call Gemini, DeepSeek and other “free/low-cost models” directly from the Claude Code UI. Comes with a Web UI and one-click upgrades.
BMAD enterprise workflows: Built-in multi-role agents for product, project, architecture, development, testing, etc., supporting both greenfield and legacy projects, with auto-generated docs; /bmad-init initializes with one command.
Practical Git command set: /git-commit for smart commits, /git-rollback for safe rollbacks, /git-cleanBranches to clean branches. Turns common Git operations into guided flows.
All of the above is clearly documented with examples in the project README’s Quick Start, CCR, BMAD, installation flow, and install options list.
# Claude Code already installed, only update workflows
npxzcfu
# Open CCR management (cost-saving router)
npxzcfccr
# Usage statistics (ccusage)
npxzcfccu
The menu lets you choose: interface language, AI output language, AI persona, auth method (Auth Token or API Key), whether to use CCR, whether to install MCP. Backups and merge strategies are built-in throughout for peace of mind.
Run npx zcf i for full initialization → after launching claude, type /init to auto-generate CLAUDE.md, so the AI first understands your project’s skeleton.
For small tasks, just write natural language; follow SOLID/KISS/DRY/YAGNI with a minimal-change mindset.
/feat <task>: Automatically enters the two phases of Planning → UI, ideal for page/interaction requirements.
/workflow <task>: Presents multiple approaches, asks for your feedback at each step, and supports on-the-fly plan changes — much stronger controllability.
/bmad-init boots BMAD team workflows, defining roles, quality gates, and documentation output — great for multi-person parallel work and legacy system remediation.
Language and persona: In the menu, you can set AI output language and Assistant persona (Professional, Friendly, etc.), which significantly influence communication style and output quality.
MCP services: Optional services like Context7, DeepWiki, Playwright, Exa — enable as needed to avoid unnecessary costs.
Solid on Windows / Termux: ZCF includes platform adaptation and formatting fixes, plus sturdier command detection.
ZCF streamlines the “setup configs, organize workflows, control costs” grind into a single sweep.
Imagine this: a new teammate joins, runs npx zcf i, grabs a drink, and the project is ready to run.
Which part would you try first — CCR cost-saving routing or BMAD team workflows? Share in the comments.
Use Claude Code as a “control center.” With a custom slash command, hand the task off to the Cursor Agent CLI so that GPT-5 can modify code locally, run commands, and report results back.
GPT-5 is OpenAI’s most capable coding model to date, with improvements for real-world engineering scenarios and long-horizon agent tasks. The company explicitly highlights its strengths in “agentic coding” products such as Cursor, Windsurf, GitHub Copilot, and Codex CLI. Codex CLI also sets GPT-5 as the default/recommended model.
The screenshot shows a configuration snippet in .claude/agents/gpt-5.md:
name: gpt-5 — describes this “sub-agent” as suitable for deep research, second opinions, and bug fixing.
tools: Bash, model: sonnet.
The system prompt positions it as a “senior software architect.”
It includes an example command: cursor-agent -p "TASK and CONTEXT", then reports the result back to the user.
This is the pattern: Claude handles orchestration; the hands-on work is delegated to an external agent (Cursor).
It’s essentially a set of “common directives in Markdown,” stored at the project or user level and invoked anytime with /name arguments.
Storage locations: project-level .claude/commands/; user-level ~/.claude/commands/. The filename is the command name.
Optional Frontmatter: description, model, argument-hint, allowed-tools, etc.
Supports injecting Bash output into context: prefix with ! to execute, e.g., !git status. You must declare allowed Bash commands in the frontmatter.
Supports $ARGUMENTS placeholders, @file references, and namespaces (subdirectories enable names like /frontend:build).
Use case: let external CLIs handle “assembling, pulling code, running tests, generating patches,” while Claude coordinates and summarizes. The official docs explicitly support this workflow.
Save the following as .claude/commands/cursor-gpt-5.md:
---
name: gpt-5
description: Use this agent when you need to use gpt-5 for deep research, second opinion or fixing a bug. Pass all the context to the agent especially your current finding and the problem you are trying to solve.
tools: Bash
model: sonnet
---
You are a senior software architect specializing in rapid codebase analysis and comprehension. Your expertise lies in using gpt-5 for deep research, second opinion or fixing a bug. Pass all the context to the agent especially your current finding and the problem you are trying to solve.
Run the following command to get the latest version of the codebase:
This quick-start checklist is for those who build products, write code, or design.
I split the latest “AI Power List” by use case, and for each category only cover why it’s chosen + how to use it.
No long reports—read it and put it to work immediately.
General-purpose frontier models got stronger: GPT-5, Claude Opus 4.1, Qwen3, Kimi K2 and others noticeably improved in reasoning, front-end generation, and agent tasks.
Vertical tools matured: Video (Sora, Veo 3, Runway Gen-3), music (Suno 4.5+, Eleven Music), and design (Midjourney, Ideogram, Recraft) are now stable, fast, and great-looking.
General-Purpose Models (Tackle Hard Problems, Run Projects, Brainstorm Partner)
1) GPT-5 (top pick)
Exceptional front-end/multimodal coding—can generate production-ready responsive UIs in one go, with better aesthetics for typography and whitespace. Ideal for turning “sketchy ideas” straight into clickable pages/micro-sites.
Getting started: Provide Figma screenshots + interaction notes; request a runnable project in Next.js + Tailwind + shadcn/ui.
2) Claude Opus 4.1 (code and agent tasks)
Strengthened real-world coding and multi-step reasoning; within Claude Code you can execute workflows directly—great for an engineering flow of “plan first, then execute.”
3) Qwen3 (Chinese ecosystem + flexible local/cloud)
Flagship model now competitive in code, math, and general abilities; easy enterprise integration within Alibaba’s ecosystem.
4) DeepSeek-R1 (value and open-source vibe)
Open weights and friendlier licensing; stronger RL-style reasoning; suitable for low-cost self-hosted inference and distillation.
5) Kimi K2 / GLM-4.5 / Grok 4 (niche strengths)
K2: Large-scale MoE, longer context, stronger tool use; smooth for Chinese scenarios.
GLM-4.5: Emphasizes reasoning, coding, and agent use cases.
Grok 4: Strong at native tool use + real-time search scenarios.
Want to experiment locally? Watch for OpenAI’s GPT-OSS-120B/20B open weights—good targets for high-end local rigs.
Midjourney: Top-tier for texture and stylization; even lighting details hold up.
Ideogram: Outstanding typography/legibility—great for KV, banners, and headline lockups.
Recraft: One-click editable vectors and flat illustrations; easy to maintain brand consistency.
Qwen-Image: Strong Chinese text rendering and precision edits—useful for domestic needs.
Imagen (Google): Realism, typography, and safety watermarking are more comprehensive.
Practical tip: Provide 3 style directions + 1 “avoid” constraint (e.g., “no oversharpening”), and request layered PSD/SVG for easier post-production.
Text-to-Video (Ad Cuts, App Store Trailers, Gameplay Demos)
Sora: Strong visual quality + physical consistency; can generate 20s 1080p with iterative editing. Ideal for concept pieces/gameplay demos.
Runway Gen-3: Better prompt adherence and motion coherence; highly production-ready.
Luma Dream Machine (Ray2): Fast cinematic motion results; included in subscription.
Veo 3 (Google): Native audio, lip-sync, and image-to-video now in Gemini/Vertex.
Wan 2.2: High playability in the open-source community; good for hybrid local/cloud experiments.
Execution advice: Break your script into a shot list (shot size/duration/subject/camera move), generate per shot, then assemble with Runway/CapCut.
AI Music and Voice (Game OSTs, Short-Form BGM, Podcast Intros)
Suno v4.5+: Versatile styles and more realistic vocals; adds production-grade features like add vocals/add accompaniment/Inspire.
Eleven Music: Fully commercial focus with Merlin/Kobalt licensing—suited for brands and commercial video scoring.
Udio: Nuanced vocal emotion and easy UX—great for TikTok/YouTube assets.
Workflow: Prototype with Suno/Eleven, lock BPM/emotional curve, then iterate lyrics and instrumentation.
Speech Synthesis & ASR (Voiceover/Dubbing/Subtitles/Notes)
ElevenLabs TTS: 70+ languages, fine-grained emotion control, mature API.
Whisper: Veteran open-source ASR with high-performance variants like whisper.cpp / faster-whisper; the cost-effective choice for local deployments.
For high-risk domains (medical/legal), ensure human review to avoid misrecognition issues.
Claude Code: End-to-end plan → execute → review for complex tasks, using “structured dialogue” to drive delivery.
Cursor / Copilot / Windsurf: Mature day-to-day ergonomics for incremental dev and multi-file refactors.
Trae / Kiro / Gemini CLI: Consider these as lightweight alternatives for team collaboration, mobile, or CLI workflows.
OpenRouter / Replicate / Fal: One gateway to many models & generative services; enables dynamic cost/perf routing.
Practical stack: Use Cursor/Claude Code at the frontend; route models via OpenRouter; send image/video special tasks to Replicate/Fal.
Dify (self-host/enterprise-ready): Visual agent workflows + RAG + observability; runs in cloud or on-prem.
n8n: AI nodes + 400+ integrations, wiring up CRM/tickets/databases; plenty of templates.
Coze: No-code for bots/helpers; easy promotion to multiple platforms for domestic teams.
Opal (Google Labs): Compose mini AI apps/flows in natural language—great for PMs to build quick prototypes.
OCR / Document Structuring (Turn PDFs into “Learnable/Queryable” Markdown)
Don’t file this under “to read later.”
Pick one scenario + one model and run a small pipeline today.
Add one more step a week later, and you’ve built your personal AI production line.
Which one will you ship first? Tell me in the comments.
Conclusion first: GPT-5 excels at planning, structured design, and review/summary; Claude Code (Sonnet / Opus) is more reliable for hands-on execution and refactoring. In practice, the combo “GPT-5 plans + Claude Code builds + GPT-5 reviews” yields higher efficiency and fewer failures. What’s your take?
After the keynote, my first reaction was: GPT-5 isn’t just faster and more accurate—it thinks better. OpenAI exposed minimal reasoning and verbosity as tunable parameters, expanded context windows to 400K tokens with up to 128K output, and embedded Gmail/Google Calendar connectors directly into ChatGPT—available for Team today, with Enterprise/Education on August 14.
Media sentiment is consistent: fewer hallucinations, stronger coding, more “expert-like.” But a caveat—marketing charts backfired. Don’t trust vendor curves blindly; prioritize independent evaluations and real projects.
Controllable “thinking intensity”: API adds reasoning_effort="minimal", enabling faster responses without sacrificing much quality; verbosity dials answer length and information density. For engineers, these are your speed/quality knobs.
Much longer “working memory”: 400K context (about 272k in + 128k out) improves long-document handling and long-chain tool use.
Transparent pricing: $1.25 per 1M input tokens; $10 per 1M output tokens; mini/nano are cheaper.
Toolchain collaboration: OpenAI claims substantial gains on complex tool use (τ²-bench telecom scenario: 96.7%), fitting end-to-end agent workflows.
New ChatGPT experience: Mail and calendar connectors, personalized tone/voice, learning mode—lowering friction for everyday and team use.
In short: GPT-5 is a more steerable generalist—able to think shallow or deep on demand, and better at tools.
These are subjective notes from real-repo work—feel free to critique.
Blank-page code often misfires: Asking GPT-5 to write large backend blocks can show small API/stack mismatches—things that look right but break at runtime; Claude Code Sonnet / Opus feels steadier at “doing the painstaking work.”Opus 4.1 reports 74.5% on SWE-bench Verified, and it shows.
“Extend/modify by example” works great: Provide the project tree + key files + a minimal working example; GPT-5 follows style and integrates with existing interfaces more reliably, especially for UI/interaction—often green on first pass. Wired and TechCrunch also highlight broad improvements in coding and writing/analysis.
Benchmarks are strong—don’t deify them: OpenAI cites SWE-bench Verified 74.9%, Aider polyglot 88%; third parties show scenarios where GPT-5 ≈ Opus 4.1 ≈ Grok 4—differences vary by task. Don’t ship on scores alone.
My takeaway: I prefer GPT-5 for “blueprints” and “reviews”; for actual wrench-turning, Claude Code is less hassle.
3. Recommended workflow: GPT-5 plans + Claude Code executes
Step 1 | Product/architecture design
Give GPT-5 the business goals, constraints, and non-functionals, and ask for system sketches, interface contracts, failure cases, and monitoring points. Prompt tip:
“Use verbosity=low for the summary; then high to expand risks by module.”
Step 2 | Task breakdown and acceptance
Have GPT-5 produce milestones → tasks → acceptance criteria, and a code change checklist (files, functions, risks).
Step 3 | Hand off to Claude Code
Provide the repo + task card + acceptance criteria to Sonnet/Opus; require branch-based development, stepwise commits, and rollback on failure. Opus 4.1 is particularly stable for multi-file refactors and debugging.
Step 4 | Two-way review
Let GPT-5 handle PR review, test additions, and changelogs; let Claude handle fixes and polish.
Step 5 | Long-chain automation
For complex pipelines (search/retrieval/many tools), favor GPT-5—OpenAI provides fresh evidence on long chains and instruction-following. But add human checkpoints at critical stages—don’t go fully hands-off.
Keynote charts had issues—OpenAI acknowledged the “chart fiasco.” Rely on the official docs and product pages.
Independent evaluations are incoming: Platforms like Vellum offer grounded cross-model comparisons; winners differ by dataset. Translate the problem into your real workflow—that’s what matters.
GPT-5 is the “better thinker,” Claude Code is the “better doer.” Right fit: let GPT-5 set strategy, draft blueprints, and supervise; let Claude Code execute, stitch, and refine. In 2025, this combo is the most reliable engineering play.
How would you split the work? Share your combo in the comments.
CCPlugins is a collection of 24 “enterprise-grade” commands maintained by community developer brennercruvinel, specifically crafted for Claude Code CLI. It’s all about saving time, reducing stress, and eliminating bugs. Using conversational syntax, it transforms tedious tasks like security checks, code cleanup, feature scaffolding, and smart commits into simple slash commands. The installation script runs in 30 seconds, and real-world testing shows it can save developers 3-5 hours of mechanical work per week.
“Add a button” and Claude rewrites your entire UI? That’s not uncommon in Claude Code. CCPlugins aims to cage Claude’s ‘infinite imagination’ and make it do exactly what you want.
CCPlugins upgrades “one-sentence AI commands” into modular, reusable, auditable workflows, allowing us to truly spend time on design and decision-making rather than debugging and manual grunt work. Why not install it and give it a try?
Claude Code Web UI (abbreviated as CUI) brings Claude Code from the terminal into your browser with a modern interface, parallel multi-session support, push notifications, and voice dictation. Simply run npx cui-server to launch, then log in to continue your AI programming tasks from any device.
Still stuck having “black screen conversations” with Claude in the command line? CUI upgrades your AI programming experience to a cloud IDE, letting you write prompts, run agents, and receive notifications right in your browser, with mobile device continuity support.
The terminal will output a #token string, copy it for use.
Browser Access
Open http://localhost:3001/#<token> to log in.
TIP:
Need remote access? Set server.host to 0.0.0.0 in ~/.cui/config.json, or use --host / --port parameters, then pair with Caddy reverse proxy to upgrade to HTTPS.
CUI creates a “browser-visualized shell” for Claude Code’s capabilities, with lightweight installation + remote access + push ecosystem, making AI programming as smooth as browsing the web. What pain points are you most excited to solve with it? Share in the comments!
You’ve probably heard this classic take: “A 1× engineer using AI becomes 0.1×, but a 10× engineer using AI becomes 100×.” This isn’t hyperbole—it’s reality, provided you possess key capabilities like systems thinking, precise communication, and architectural design. This article breaks down each element, combining Claude Code’s real capabilities to show you how to amplify AI effectiveness by 100 times.
Scenario 1: Systems Thinking vs File-Level Thinking 🚀
A 1× engineer can only think about “writing a method,” while a 100× engineer maintains the entire system’s state in their mind. Claude Code can instantly establish a holistic view of millions of lines of code. With just one sentence, you can have it “search user permission logic across the project, analyze cross-service dependencies, and generate a PR containing all modifications”—Claude handles the entire codebase-level coordination.
Example
You want to switch authToken encryption from HMAC-SHA256 to RSA: Simply say “rewrite token logic with RSA and ensure tests pass green.” Claude Code operates across 5 files in one go: controller, service, utils, and unit tests, creating a coordinated Git commit.
Scenario 2: Communication Precision: The Efficiency Multiplier
A 1× engineer provides vague requirements, forcing Claude to “guess” and “try,” leading to endless iterations.
Those 100× engineers who master engineering-minded communication know that prompts written like good articles enable LLMs to execute precisely.
The Claude team repeatedly emphasizes structured prompts: including background, intent, format, examples, and validation criteria.
Example
You want to refactor the calculateScore function while maintaining logical consistency:
Provide sample input/output tables, desired PR title templates, and CI validation rules; then tell Claude Code in one sentence: “Based on the following examples and rules, refactor calculateScore and output PR diff, test report, and commit message.”
Result: 99% PR pass rate, 20× reduction in manual curation.
Scenario 3: Patient Architecture Refinement: The Foundation for Stable AI Output
A 1× engineer rushes Claude to write features without looking back, resulting in system crashes; a 100× engineer knows that upfront design is crucial.
Claude Code can combine long-context memory with engineering intent to execute well-designed structural changes—enter MCP (Model Context Protocol), enabling Claude to access your design documents, tickets, architecture diagrams, etc., forming a long-term perspective (state).
Example
You plan to split a monolith into service mesh: First write clear documentation + interface contracts + merge strategies, then use Claude Code to step-by-step generate scaffolding, extract logic, generate SDKs and prototype PRs, even auto-generate CI configurations—only passing to you for review after tests pass.
Scenario 4: Context Control: Your Value Multiplication Lever
Stop treating LLMs as autocomplete and start using them as system-level contextual engines. Claude Code embeds Claude Opus 4, capable of fully understanding entire project structures, dependency graphs, naming conventions, style guidelines, even READMEs and issue templates.
Example
You want to add a “mobile caching layer” module. Just tell Claude Code to “follow the project’s existing cache-service pattern, generate consistent module + README + unit tests,” and it will recognize the pattern, consistently inserting new code into the architecture.
Scenario 5: True Intelligence and Systems Thinking First, Tool Knowledge Optional
A 1× engineer relies on IDE shortcuts memorization, a 10× engineer switches from Node.js to Go, but a 100× engineer doesn’t enjoy writing code—they enjoy proposing patterns, deconstructing problems, and letting agents execute.
Claude Opus 4 is internally called “the best coding model in the world” at Anthropic, enabling engineers to “persistently complete complex tasks with hundreds of steps within hours”—this is the typical outcome of systems thinking + agent control.
Example
You want to rewrite a microservice, migrating from Express to Rust while preserving all original behavioral boundaries: You write requirements and sample data, Claude Code automatically scaffolds the Rust service, generates HTTP interfaces, maps data models, migrates old logic, and even generates comparison tests and deployment configs.
Scenario 6: AI is a Powerful Amplifier—Your Strengths Make AI Stronger, Your Weaknesses Make AI Worse
For 100× engineers, Claude will “learn” your organizational principles, naming conventions, test coverage, documentation style and consistently execute them in every commit. If you write messy English comments and use inconsistent indentation, it will help you continue the mess; if you have discipline, it transforms you into a stylized engine.
Example
Early in your project, you strictly follow <service>.test.js, commit descriptions over 90 characters, complete READMEs and issue templates. Claude Code will be equally strict about marking "BREAKING CHANGE", won’t generate code for untested paths, and automatically reference issue IDs in commits. If the project is chaotic, its generated commits will often be chaotic too—this “mount” follows whoever’s rhythm.
Real community feedback:
“I have mindlessly asked Claude Code over a large codebase … it gave an extremely clear report…” —— Even with poor prompts, clear project structure makes Claude Code quite effective.
Claude Code isn’t just intelligent code writing—it enables you to think about system problems like an architect, communicate design intent precisely, and continuously maintain code standards.
It doesn’t eliminate coding but frees your brain for truly important decisions, delegating heavy repetitive tasks to AI.
When you polish systems thinking, prompt crafting, project structure, test coverage, and engineering standards, Claude becomes your zero-error 100× amplifier.
Claude Code Security Review: Making Security Audits as Easy as Writing Commands
Claude Code’s newly released claude-code-security-review integrates Anthropic Claude’s large language model reasoning capabilities directly into GitHub Actions and terminal commands. It can analyze diffs during pull request (PR) stages to identify high-risk vulnerabilities like SQL injection, XSS, and authorization bypasses, while automatically filtering out low-value noise. You can even scan locally with a simple /security-review command. Your code’s potential vulnerabilities are flagged by AI before it even gets merged.
Built-in False Positive Filtering automatically screens out low-risk alerts like DoS, resource exhaustion, and input validation with no actual impact, surfacing only the truly critical vulnerabilities.
Identifies 10 major categories of issues including injection attacks, authentication flaws, hardcoded secrets, weak encryption, and business logic race conditions.
Publications like VentureBeat and The New Stack have noted that as AI-generated code volume surges, automated security auditing has become essential. This Action fills that critical gap.
AI writes code fast, but AI must guard security even faster. Integrate claude-code-security-review into your DevSecOps pipeline to ensure every line of code passes through security gates before entering the main branch. The next zero-day might just be the one you keep out.
Backlog.md + Claude Code transforms any Git repository into a Kanban board + documentation + AI collaboration hub in seconds, with all data stored as Markdown files for offline privacy. This article will get you up and running in 5 minutes, covering installation, core features, and typical workflows to explain why it’s the minimal viable stack for human + AI collaborative projects.
Tired of switching between multiple SaaS tools? Backlog.md generates a complete project management system locally with a single command—perfect for individual developers and seamlessly integrates with multi-agent pipelines, allowing Claude and Gemini to directly read and write tasks.
Backlog.md uses the most native toolchain to unify project management, documentation, and AI collaboration into one system, helping us refocus attention on “writing code” itself.
Ready to try this “local Git kanban” approach? Share your experience in the comments!
Have you ever wished you could package common AI operations into shortcuts that execute with a single command, while having AI break down tasks and collaborate through division of labor? Claude Code is precisely such a tool that enables developers to use AI-assisted coding more efficiently, systematically, and enjoyably. Today I’ll discuss its two core features in accessible terms: Slash-Commands and Sub-Agents, explaining their characteristics, usage, and differences, with real examples you can immediately apply.
Write custom prompts once, invoke with commands: Package common operations (like code reviews, performance optimization, issue fixes) into .claude/commands/*.md files, and Claude treats these prompts as commands. Simply type /command-name parameters to automatically execute the corresponding workflow.
Parameterization support: Commands can use $ARGUMENTS placeholders that get replaced when you input parameters, flexibly accepting issue numbers, module names, and other arguments.
Project sharing or personal use: Place in .claude/commands/ for project-level commands accessible to the entire team; place in ~/.claude/commands/ for personal private commands.
Claude will perform Git operations, code modifications, and open Pull Requests - the entire process is automated, saving you countless repetitive steps (you can also choose whether to auto-commit). Perfect for scenarios like repeatedly fixing issues, reviewing PRs, and generating documentation.
Sub-Agents: Multi-Agent System with AI Task Decomposition and Collaborative Division of Labor 🧠
Each sub-agent stays focused: Sub-Agents are “mini Claudes” with independent contexts, specializing in specific task types like performance optimization, test generation, code review, etc., never interfering with the main conversation thread.
Customizable roles, reusable: You can use the /agents command to create sub-agent configurations, each with clear names, descriptions, and permissions (like read-only code access, running tests, calling grep, etc.).
Main agent orchestrates and can chain multiple sub-agents: The main Claude decides which sub-agents execute which tasks based on context, supporting task decomposition with clear responsibilities, with final results aggregated back to the main conversation interface.
code-reviewer.md: Only reviews code style and security issues
test-builder.md: Specializes in generating unit tests
debugger.md: Responsible for checking error logs and locating problems
Your conversation with Claude goes like this:
“Please review the auth module” → Main agent calls code-reviewer
“Write unit tests for this module” → Calls test-builder
“Found test failures, find where the error is” → Calls debugger
Finally, the main agent summarizes the output for you. The entire process is like having a team of AI assistants, each handling their specialty.
Key Differences: Different Invocation Methods, Different Division Perspectives
Write a Slash-Command /security-review with templates that automatically check code security points.
Main agent calls security-review, then triggers code-reviewer sub-agent for detailed output based on circumstances.
Automated Bug Fix + Ticketing
Slash-Command /fix-issue 456 initiates: Main agent scans GitHub issue → calls debugger to check errors → modifies code → calls test-builder to write tests → finally opens Pull Request.
Module Collaboration Scenarios
Create frontend-expert sub-agent focused on React component optimization, create backend-optimizer sub-agent responsible for API performance.
When the project needs a new feature, main agent runs one workflow letting both sub-agents produce their respective solutions, then aggregates for your decision-making.
Slash-Commands are your shortcut command buttons, suitable for repetitive, standardized operations
Sub-Agents are your behind-the-scenes AI team, capable of task decomposition, multi-perspective analysis, and specialized division of labor
I recommend starting with Slash-Commands: Put your most frequent operations into commands and share template files with your team.
After becoming proficient, gradually introduce sub-agents to handle complex workflows through task decomposition, with the main agent orchestrating while you maintain control. This approach avoids detours and prevents initial tool overwhelm.
For small teams or short projects, one or two Slash-Commands and simple sub-agents are quite sufficient; for long-term projects or multi-person teams, you can gradually expand into a complete AI collaboration pipeline. It’s like hiring a reliable AI assistant team - both development efficiency and standardization will improve significantly.
Many people use Claude Code for programming, but have you ever felt that:
Despite Claude being powerful, you always have to guide it step by step
Every time you need testing, documentation updates, or PR generation, you have to remind it manually
Role switching becomes chaotic and actually reduces efficiency instead of improving it
Recently, Japanese engineer @wasabeef released an incredible project — claude-code-cookbook,
transforming Claude Code into an “automated development assistant” that works fast, provides accurate feedback, and thinks clearly.
This isn’t just another prompt template collection, but Claude’s “extended operating system.”
👇 I’ve been testing it for several days and it’s absolutely impressive. Highly recommended for anyone using Claude for development projects!
🔧 Three Core Features: Unlocking Claude’s Hidden Capabilities
Claude supports switching between different roles like “sub-personalities,” such as:
/role qa Test Engineer
/role mobile Mobile Development Expert
/role security Security Consultant
/role architect System Architect
🌟 The highlight: supports concurrent execution!
Terminal window
/roleperformance--agent
/rolesecurity--agent
Claude will simultaneously launch multiple roles for parallel analysis and provide unified recommendations.
This is incredibly useful for large project reviews and design assessments.
⛓ 3. Hooks: Automated Scripts for Maximum Development Experience
Still using Claude Code just for writing code? I’ve moved on to using Claude Code for managing my entire life.
This isn’t a joke—I’ve built a complete “life operating system” with Claude Code that effortlessly manages my daily routines, captures inspiration, tracks goals, and even takes care of my mental wellbeing.
With just 10 minutes of setup, Claude helps me complete countless small but important tasks every day.
Anthropic just introduced “weekly quotas,” and shared team accounts can hit limits in just 3 days. Build your own claude-relay-service proxy with multi-account pool auto-rotation to bypass the latest restrictions. Deploy with Docker-Compose in 3 minutes, give everyone their own API key, and keep coding all week long!
After startup, open http://<server-IP>:3000/web in your browser, or locally http://localhost:3000/web. The logs will show the auto-generated/custom admin account.
One Key Per Person: Generate API keys for each team member in the “API Keys” panel, with rate/model limits to prevent abuse.
Environment Variables: Set ANTHROPIC_BASE_URL to http://localhost:3000/api/ and ANTHROPIC_AUTH_TOKEN to the generated key in environment variables, no business code changes needed.
Compliance Reminder: Anthropic has explicitly prohibited reselling and account sharing; accounts may be banned or throttled if abnormal traffic is detected.
Deploy your own “mini-relay” now and keep quota control in your hands.
Forward this to colleagues worried about throttling - let’s maximize usage together!
Claude-Code-Remote brings terminal-based Claude Code to “Email / Discord / Telegram”, letting you remotely send commands, receive progress updates, and continue tasks while lounging on the couch scrolling your phone, truly enabling 24 × 7 remote collaboration and long-task hosting.
Running Claude Code for unit tests, refactoring, and document generation takes tens of minutes, even hours.
Previously, you had to sit in front of your computer watching tmux windows. Now, just one email gets you “task completed” notifications, and you can reply to the email with new commands — this is the experience Claude-Code-Remote delivers.
Claude-Code-Remote breaks down “waiting → notification → secondary command” into three parts, using the most universal email/IM channels to completely decouple Claude from the terminal, letting AI agents truly run in the background while humans focus on high-value decisions.
If you’re tired of staring at dark Shell windows, install it and give it a try!
How much time do you think this remote control method could save you? 👇
Share in the comments — your real-world experience might become the next case study!
Summary:
Anthropic announced that starting August 28th, Claude Code subscribers will face additional “weekly caps” with separate weekly limits for the top-tier Opus 4 model. The company directly stated: some people treat Claude as a “24×7 coding mining machine” and even share accounts and resell quotas. The new rules aim to “only affect less than 5% of users,” but have ignited the developer community — many “freeloader exploiters” lament they can no longer harvest GPU computing power.
Remember the days of unlimited Claude Code access through “shared accounts + automated scripts”? Those good times are over. Anthropic sent emails to subscribers announcing a stricter weekly cap system, with just one reason: fairness + stability.
“Some power users run Claude in the background 24 hours a day, even reselling access permissions, slowing everyone down.” — Official email
Anthropic’s move, while ostensibly targeting “freeloaders,” is essentially a resource redistribution in the era of computing scarcity: letting 95% of normal users enjoy stable experiences while making ultra-high-frequency players pay for their usage. Will this reduce your reliance on Claude Code? Welcome to share your thoughts in the comments!
Interaction:
How many hours do you use Claude per week?
If you hit the cap, which tool would you switch to?
Claude Code UI is an open-source Web/mobile interface that connects to the Claude Code CLI on local or remote servers, providing conversation, file and Git browsing, built-in Shell, session management and other capabilities; high-risk tools are disabled by default, supporting on-demand enabling and permission control. The project uses GPL-3.0 license and is actively maintained.
It brings the terminal-based Claude Code to browsers and phones, helping you manage sessions and projects more conveniently on any device.
Break Free from Pure Command Line Limitations: Official Claude Code is primarily CLI-based, powerful but not friendly to beginners/mobile users; UI structures it into web pages, lowering the operational barrier.
Cross-Device Lightweight Remote Usage: View projects, switch sessions, review changes on iPad/phone, suitable for commuting, meetings, or outdoor scenarios.
Centralize Common Development Actions to One Interface: Conversation, file editing, Git operations, terminal and session history are centrally presented, reducing context switching.
Desktop & Mobile Responsive
Responsive layout, usable on desktop/tablet/phone, can be added to home screen as shortcut (PWA experience).
Conversational Interface + Real-time Streaming Output
Built-in chat panel and session management, supports resuming historical sessions, viewing timeline and metadata.
Integrated Shell Terminal
Directly access Claude Code CLI’s command line capabilities within the UI (like executing tasks, viewing logs).
File Browsing & Online Editing
Left sidebar file tree, syntax highlighting, read/write/save, create/rename/delete files and directories.
Git Browsing & Basic Operations
View, stage, commit and switch branches in the interface; recent versions also added push/pull/fetch.
Project & Session Auto-Discovery
Automatically collect projects from ~/.claude/projects/; sessions auto-persist and can be exported.
Secure Defaults & Permission/Tool SwitchesAll tools disabled by default, enable individually as needed in settings; supports alignment with Anthropic’s Permission Modes (introduced in Plan mode updates).
Authentication & Multi-User Ready
Starting from 1.1.4, introduced login/registration flow with SQLite-based authentication and protected routes.
Continuous Iteration
As of v1.5.0 (2025-07-13), supports adding MCP servers in settings; previous versions included version management prompts, Vite 7 upgrades, etc.
Note: The official installation page states Node.js 16+, while the repository README requires Node.js 20+; follow the repository README, recommend using Node.js 20+.
What happens when your team wants to use Anthropic’s Claude AI together, but struggles with account sharing limitations or regional restrictions preventing direct access to Claude? This was exactly the dilemma faced by colleagues Li and Wang: they had pooled resources to subscribe to Claude’s premium tier, but figuring out how to safely and efficiently share access among team members was proving challenging. To make matters worse, many third-party Claude mirror services on the market are often unstable and pose privacy risks. Fortunately, the open-source project Claude Relay Service has emerged as a game-changer, making it possible for teams to build their own Claude relay service! It supports consolidating multiple Claude accounts in one place for shared team use, while providing OpenAI-compatible APIs for easy integration with various applications. This article will dive deep into the project’s core features and usage methods, guiding you step-by-step through building your own Claude relay station to achieve efficient team Claude account sharing. After reading this guide, you’ll discover that sharing Claude with teammates is like co-leasing an “AI supercar” – both cost-effective and worry-free!
Claude Relay Service acts as your self-deployed Claude “relay server” – like a butler that manages multiple Claude accounts and provides unified API endpoints for team access. Here are its key capabilities:
Team Account Pool & Multi-Account Rotation: Supports adding multiple Claude accounts to form an account pool, with the service automatically rotating through them to handle requests. This means if one account reaches its usage limit or encounters issues, the system will intelligently switch to the next account, ensuring uninterrupted service. When teams share Claude accounts, each person’s requests are evenly distributed across different accounts, significantly reducing the risk of individual account bans.
OpenAI API Compatibility: Claude Relay Service provides both native Claude interfaces and OpenAI-compatible interfaces. This means you can call Claude using OpenAI API format! For example, by pointing third-party applications’ API endpoints to http://<your-server>:3000/openai/claude/v1/, you can use Claude models just like calling OpenAI. This feature enables seamless integration of existing AI tools with Claude, eliminating the need to modify code for new interface compatibility.
Custom API Keys: For convenient team management, administrators can generate independent API Keys for each user. Team members access Claude Relay Service using their respective keys, while the relay service tracks request counts and token usage for each key. Administrators can set access permissions and rate limits, such as maximum requests per minute, monthly token quotas, or restrict which models are available. This approach allows teams to share Claude accounts while maintaining individual usage tracking and preventing resource conflicts, giving administrators clear visibility into each person’s usage patterns.
OAuth Quick Account Addition: Adding Claude accounts to the account pool is incredibly simple. Claude Relay Service integrates Anthropic’s OAuth authorization flow: administrators click “Add Account” in the web interface, generate an authorization link, then log into their Claude account to authorize and integrate it into the service. The entire process requires no manual password entry – secure and convenient. Note that if your server is located in regions with restricted access, completing OAuth authorization may require VPN assistance. With OAuth integration, even as teams add new members or change Claude accounts, administrators can complete configuration in minutes with seamless transitions.
These combined features create a secure, efficient, flexible, and controllable Claude sharing platform. For small teams unable to directly access Claude’s official service who want to share Claude subscription costs, this is undoubtedly a blessing! Claude Relay Service lets you avoid unstable third-party mirrors while maintaining control over your data and costs. As the official README states: data security, controllable performance, transparent costs – the benefits of self-hosted services are clear at a glance.
Claude Relay Service offers multiple deployment options, with Docker Compose deployment being one of the simplest and most efficient choices. Let’s use Docker Compose as an example to build your Claude relay service from scratch.
1. Environment Preparation: Ensure your server has Docker and docker-compose tools installed. Since we’re using Docker containers, the operating system doesn’t matter much – as long as it can run Docker. The official recommendation is to use overseas cloud servers (US nodes) for direct access to Anthropic’s Claude service. Before deployment, you’ll need to register at least one Claude account and subscribe to the appropriate plan (teams typically choose Claude Pro or Max plans and split the costs). Additionally, prepare two security keys: JWT_SECRET and ENCRYPTION_KEY, used for JWT signing and sensitive data encryption respectively – these must be random strings with sufficient length (32+ characters).
2. Get Project Image: The project provides pre-built Docker images that can be pulled directly:
Terminal window
dockerpullweishaw/claude-relay-service:latest
The image supports both AMD64 and ARM64 architectures. Once pulled, we can run it using Docker Compose.
3. Write Configuration: Create a .env file in your deployment directory with the environment variables we prepared:
# Optional: preset admin account (random generation if not set)
ADMIN_USERNAME=cr_admin
ADMIN_PASSWORD=your-secure-password
Above, JWT_SECRET and ENCRYPTION_KEY are required fields – please replace them with your own random keys. ADMIN_USERNAME and ADMIN_PASSWORD are optional; if not set, the system will automatically generate default admin credentials on first startup and store them in the mounted data volume for reference.
Next, create the docker-compose.yml configuration file:
version: '3.8'
services:
claude-relay:
image: weishaw/claude-relay-service:latest
container_name: claude-relay-service
restart: unless-stopped
ports:
- "3000:3000"
environment:
- JWT_SECRET=${JWT_SECRET}
- ENCRYPTION_KEY=${ENCRYPTION_KEY}
- REDIS_HOST=redis
- ADMIN_USERNAME=${ADMIN_USERNAME:-}
- ADMIN_PASSWORD=${ADMIN_PASSWORD:-}
volumes:
- ./logs:/app/logs
- ./data:/app/data
depends_on:
- redis
redis:
image: redis:7-alpine
container_name: claude-relay-redis
restart: unless-stopped
volumes:
- redis_data:/data
volumes:
redis_data:
The Compose file above defines two services: claude-relay and redis. Let’s explain each field:
Images and Containers: The claude-relay service uses our pulled official image weishaw/claude-relay-service:latest, naming the container claude-relay-service for easy identification. The redis service uses the official redis:7-alpine lightweight image as the backend database. Both services have restart: unless-stopped set to ensure automatic restart on abnormal exits, guaranteeing high availability.
Network Ports: ports maps the container’s internal port 3000 to the host’s port 3000, meaning after startup you can access Claude Relay Service’s API and web management interface via http://<server-IP>:3000.
Environment Variables: environment lists the environment variables required by Claude Relay Service. ${JWT_SECRET} and ${ENCRYPTION_KEY} read corresponding values from our .env file. REDIS_HOST=redis specifies the service should connect to the container named redis as its database. ADMIN_USERNAME and ADMIN_PASSWORD use ${VAR:-} syntax, meaning if these variables are provided in .env, use those values; otherwise leave empty for automatic admin account creation. By centralizing configuration through environment variables, no sensitive information is hardcoded in images, and configuration changes are convenient.
Volume Mounting: Claude Relay Service writes logs and data to /app/logs and /app/data directories within the container respectively. We mount the host’s ./logs and ./data directories to corresponding container paths via volumes, achieving log and data persistence. This ensures that even if containers restart or upgrade, previous conversation content, account configurations, and other data won’t be lost.
Service Dependencies: depends_on: - redis indicates the Claude Relay service should wait for the Redis service to be ready before starting. This ensures the application can successfully connect to the database on startup without worrying about startup order issues.
Named Volumes: The volumes: redis_data: at the bottom defines a persistent volume for storing Redis data files. The Redis container mounts this to its /data path, allowing Redis data to be shared/persisted across containers.
With the configuration files ready, you can see they already cover admin account initialization, data persistence, built-in Redis, health restarts, and more. Docker Compose helps us handle all these deployment complexities in one go – worry-free and effortless.
4. Start Services: In the directory containing the above files, execute:
Terminal window
docker-composeup-d
Docker will pull the Redis image and start both containers. On first startup, Claude Relay Service will automatically initialize admin credentials. If we didn’t preset admin credentials in .env earlier, we can get the default account by checking logs or files:
Terminal window
dockerlogsclaude-relay-service# Find initial admin account in container logs
# Or check mounted data files
cat./data/init.json
The logs or files will display the admin username (typically defaults to cr_admin) and a randomly generated password. With admin credentials in hand, open http://<server-IP>:3000/web in your browser to access the web management interface. Enter the admin account to log in and start configuring your Claude Relay Service!
5. Add Claude Accounts: After logging into the admin backend, first integrate Claude official accounts into the system. In the “Claude Accounts” tab, click “Add Account”, then click “Generate Authorization Link”. The system will redirect to Anthropic’s official OAuth page, prompting you to log in and authorize. Use the Claude account you want to share to complete authorization, which will return an authorization code. Paste this back into Claude Relay Service’s add account page to successfully bind the account. The entire process is similar to using third-party apps to log into Google – secure and convenient. Pro tip: If your server is in regions with restricted access, this step may require VPN assistance, otherwise you won’t be able to open Anthropic’s authorization page.
6. Distribute API Keys: With the account pool ready, you can create individual API Keys for team members. In the admin backend, go to the “API Keys” tab, click “Create New Key”, name it (e.g., “Zhang San’s Key”), then generate. You can also set permissions and quotas for that key during creation, such as maximum requests per minute, concurrency limits, available models, client binding, etc. After saving, the system generates a random API key string. Team members can then use this key to call Claude Relay Service’s API endpoints, and the relay service will query Claude on their behalf.
After completing these steps, a fully functional Claude team sharing relay service is successfully deployed! Wasn’t the entire process quite streamlined? With Docker Compose, we barely need to manually configure complex environments – one command starts all services. The official team thoughtfully considered automatic initialization and data persistence in the Compose template, making it ready to use out of the box.
To maximize Claude Relay Service’s effectiveness in team collaboration, here are some lessons learned to share:
Smart Account Pool Planning: Use multiple Claude accounts to build your account pool, ensuring they have the same subscription tier (e.g., all Claude Pro/Max subscribers) so rotation won’t be hindered by individual account performance limitations. Account quantity can be adjusted based on team size and request frequency – more people means more accounts for stability. Don’t worry about account idle time; as they say, “multiple Claude accounts on patrol means no fear of bans” – even if individual accounts get temporarily suspended by risk controls, backup accounts can step in to maintain uninterrupted service.
Leverage Usage Statistics: Administrators should regularly check Claude Relay backend’s usage statistics to understand each API Key’s request volume and token consumption. This helps maintain transparent accounting within the team: when everyone splits Claude subscription costs, everyone has a clear “understanding of the books.” If you notice abnormally high request volumes during certain periods, you can communicate promptly or adjust rate limits to prevent one person from “monopolizing” resources and affecting others.
Set Access Controls: Claude Relay Service supports rich usage restrictions for each API Key. Make good use of these features – for example, limiting individual users to no more than N requests per minute, or allowing only specific clients/applications to use them – to prevent misuse and abuse. Especially when opening access to more people or integrating with third-party applications, be sure to configure proper rate limiting and concurrency controls to protect backend Claude accounts from overload and prevent team conflicts over resource competition.
Monitor Network and Latency: Recommend deploying the service in regions with fast access to Claude servers (such as North America). If your team primarily uses it domestically, consider choosing relay routes friendly to domestic users. For example, deploy in the US but use optimized return networks to reduce latency during API calls. Based on practice, some domestic cloud providers’ overseas machines may be blocked by Claude’s Cloudflare firewall, so pay attention to official channel announcements or community feedback.
Stay Updated: This project is very active with continuously improving features (such as recently added Claude-1.2 model support, etc.). Recommend regularly following the project’s GitHub homepage or Telegram announcement channels. Docker users can periodically docker pull to get the latest images, then restart services to upgrade. Staying current allows you to enjoy more stable performance and latest feature capabilities.
Final reminder: Using Claude Relay Service constitutes unofficial Claude API access – please be sure to comply with Anthropic’s terms of service and avoid abuse that could risk your accounts. Self-hosted services give us greater freedom but also mean taking responsibility for security and compliance ourselves. Establish good usage guidelines within your team to ensure long-term “happy Claude usage”!
With Claude Relay Service, we no longer need to rack our brains over team Claude account sharing. Whether you’re unable to directly access Claude due to regional restrictions or want to split the cost of expensive Claude Max subscriptions with teammates, this open-source project provides a flexible, reliable, secure, and private solution. We’ve detailed its core features: multi-account pools, OpenAI interface compatibility, custom API keys, OAuth account addition, and more, showing you clearly how it meets team collaboration needs. At the same time, we’ve demonstrated Docker Compose deployment step-by-step, from configuring environment variables to starting services – we believe even readers who aren’t DevOps experts can follow along and quickly set up their own Claude relay station.
It’s easy to imagine that with Claude Relay Service, late-night coding sessions no longer fear Claude account quota depletion, and colleagues don’t need to each open separate accounts and pay duplicate fees. Everyone shares one “Claude resource pool,” taking what they need while keeping everything transparent and clear – truly achieving cost savings and peace of mind!
Don’t worry that Claude can’t understand Chinese, its trigger logic also supports our native language:
HIGHEST (32K): 仔细思考、深思、多想一会
MIDDLE (10K): 好好想、多想想
BASIC (4K): 思考、想
For example, if you say “Think carefully about this problem before giving me a plan” — Claude immediately switches to the highest gear, allocating the most thinking time!
Prompt:
I want to develop a “Smart Daily Report Assistant” that can automatically aggregate today’s key events from Slack, Notion, GitHub, and help me generate daily report drafts. Please think carefully (ultrathink) about this problem first, then output a module division, data flow, and API interface design.
📌 Use Cases: Multi-system integration, vague requirements, need deep architectural thinking
🧠 think hard Example: Evaluating Multiple Technical Solutions
Prompt:
I want to choose a database solution for a multi-user collaborative document application. Please think hard about whether to choose PostgreSQL + Redis or MongoDB + Kafka, output a comparative analysis and give recommendation reasons.
📌 Use Cases: Multiple routes available, need comparative analysis
Prompt:
I’m planning to write a script that downloads all image links from a Markdown file and renames them. Please think about how to do this first, which Python libraries are most suitable, then write the code for me.
📌 Use Cases: Task isn’t complex, just need a simple run-through of the approach
Prompt:
I’ve already written the usage instructions for this AI summary tool. Please optimize the following text format into a README.md file, output the result directly, no need to overthink.
📌 Use Cases: Clear requirements, just need execution, no planning needed
Claude is very capable, but if you don’t give it “thinking space”, it can only respond hastily.
Use think, think hard, think harder, ultrathink well, and you can not only improve output quality but also solve complex problems in one go.
Do you think this design is clever?
Have you ever fallen into the trap of “Claude starting work without thinking clearly”?
Welcome to share your experience in the comments 👇
claude-code-proxyautomatically converts the Anthropic API used by Claude Code to an OpenAI-compatible API. In practice that means you can switch Claude Code to GPT-4o, Gemini, Ollama—or any OpenAI-style model—without touching your source code. You’ll cut costs and still keep function calling and streaming responses.
With claude-code-proxy Claude Code becomes a true Swiss-army knife: Vue on the front end, Gemini on the back end, docs written with GPT-4o—all through one CLI port. Fire it up and tell me which model combo you like best!
In daily work, contracts, agreements, BLE communication specifications… companies are never short of PDFs.
They look beautiful but are a nightmare to edit: copy-paste gets messy, formatting goes haywire.
This article shares a practical “reverse PDF → Markdown → PDF” workflow using Claude Code in three steps, while maintaining an editable Word version.
Through Claude Code, bring out-of-control PDFs back to the editable world, then painlessly output standard PDF/Word, solving both “format unity” and “content maintainability” pain points.
What other stubborn PDFs do you have that need reviving?
Leave a comment and chat—maybe the next article will cover your scenario!
This open-source tool Claude Code Specs Generator equips Claude Code with a “documentation generator” — automatically producing 6 core documents that clearly outline business, technology, architecture, and tasks, making Claude read the manual first before writing code.
AI ≠ Mind Reading
When context is messy, Claude often “takes liberties” rewriting dependencies or reinventing the wheel. Amazon’s Kiro IDE solved the same pain point with “Spec-Driven Development”, and practice proves: generating specs first, then code, dramatically reduces error rates.
Team Collaboration Saves 30% Communication Costs
Unified product.md, tech.md, structure.md allow newcomers to complete code orientation in 10 minutes, reducing verbal handoffs.
Claude Reasoning More Accurate
Include these 6 documents in CLAUDE.md, and AI loads them with every response, avoiding “memory gaps”.
After specs are written, the generator writes file paths into CLAUDE.md. Next time you type @claude fix tests in terminal, Claude will reference the latest specs for execution.
New Project From Zero to One
Pull an empty repository, run /specs-create, instantly get “product-tech-structure” three direction documents, then iterate design and tasks.
Legacy Project Governance
Wrote lots of code and want to add documentation? Same one-click approach. The generator scans existing PRD/TODO files and merges content.
Continuous Delivery
Trigger /refresh-specs CI script with every PR, letting specs evolve alongside code.
Specs Generator = “Kiro-flavored” Free Plugin
No need to switch to a new IDE, you can enjoy the security of spec-driven development in your existing Claude Code workflow. Try /npx @kellemar/claude-code-specs-generator now, and let both AI and humans take fewer detours!
🤔 Would you add it to your own project?
Leave a comment and share which step you’d most like to automate!
(Next article we’ll break down how to turn Specs into Mermaid diagrams, stay tuned!)
This article shows you how to use one environment variable to replace Claude Code in your terminal with Moonshot’s Kimi K2 — cheap, fast, and no need to “climb mountains”. Here’s the quick conclusion first, followed by step-by-step details that you can copy and follow.
Claude Code’s Agent workflow is incredibly satisfying, but by default it only connects to Anthropic’s official models, which is expensive and has network issues.
Kimi K2 publicly provides Anthropic-compatible API, starting at 0.02 yuan per 1k tokens, about an order of magnitude cheaper than the official version; long context and coding capabilities are also solid.
Just apply for a Moonshot API Key in advance and fill in ANTHROPIC_AUTH_TOKEN and ANTHROPIC_BASE_URL, and Claude Code will treat K2 as the “genuine Claude” without needing to modify source code or use proxies.
If you see Anthropic Base URL: https://api.moonshot.cn/anthropic, it means you’re connected to K2. You can also have it write some code, and you’ll notice significant improvements in speed and cost.
Regularly check the Moonshot console to monitor K2 usage and costs.
Turn the pain points of “expensive + network issues” into the sweet spot of “cost-effective + direct connection” — just two environment variables, and Claude Code instantly switches to Kimi K2, upgrading your terminal to a domestic large model engine.
Have fun, and don’t forget to type :set language zh in claude to make the AI speak Chinese too; more new tricks in the comments!
claude-code-templates is an open-source CLI tool created by developer Daniel Avila (davila7), designed to help developers quickly configure and monitor Claude Code. It can generate project configurations with one click, analyze existing settings, and display runtime status in real-time, making it perfect for use with Claude Code .
Supports JavaScript/TS (including React, Vue, Angular, Node.js), Python (Django, Flask, FastAPI), and generic templates. Other languages like Go and Rust are coming soon .
Supports interactive startup and can also achieve one-click configuration through parameters like --language, --framework, --yes, --dry-run, --directory .
Installation command: npx claude-code-templates@latest, no global installation required. Can also be installed globally: npm install -g claude-code-templates .
Use --health-check to verify system environment, Node.js version, network connectivity, Claude Code configuration, hooks, MCP, etc., providing visual check results and recommendations .
Supports dry-run mode to preview operations, automatically backs up existing configurations, prompts for confirmation before changes, and can be canceled at any time. --yes can skip prompts for automatic execution .
“Real-time monitoring of all Claude Code sessions… Token usage charts and project activity breakdown… runs completely local” .
The community is also hotly discussing new feature requests, such as support for SwiftUI project templates .
Additionally, the author just launched an online template preview page where users can browse templates through the web, copy installation commands, and contribute their own templates to the community .
claude-code-templates is summed up in one sentence: Make Claude Code “ready-to-use, monitorable, and optimizable.”
Suitable for teams or individuals who pursue efficient development workflows, like dashboard views, and value configuration security.
Next, the project will support more languages/frameworks like Go and Rust, and open up online community template submission portals to build the ecosystem together.
Hope this article helps you quickly understand and get started with claude-code-templates, making your Claude Code development more relaxed, visual, and professional.
Open your terminal and try:
Sniffly is a local tool open-sourced by Chip Huyen that specifically parses .jsonl logs generated by Claude Code and creates an interactive web dashboard, helping you quickly answer three questions:
What you want to know…
Sniffly provides…
What did I actually use Claude Code for?
Usage Stats: command counts, file operations, average steps, etc.
What errors does Claude Code make most often?
Error Breakdown: visualize errors by type and percentage
Why did a certain conversation go wrong?
Message History: replay complete context step by step
All calculations are done locally without uploading any conversations or code, privacy-friendly.
Claude Code Router (CCR) https://github.com/musistudio/claude-code-router is a “multi-model router” tailored for Claude Code users: it listens locally to Claude Code’s standard requests, then forwards them to DeepSeek-R1, Kimi K2, and any other LLMs according to your routing strategy. It’s compatible with OpenAI-style APIs and can add “transformers” for different models to adapt parameters and responses. This article first provides an overview of CCR’s core mechanisms, then demonstrates step-by-step how to integrate DeepSeek R1 and Kimi K2, giving you everything you need for “multi-model collaborative coding.”
CCR describes Provider → Model → Transformer → Routing Rules through ~/.claude-code-router/config.json, allowing Claude Code to hot-switch backend models in VS Code/terminal as usual using /model.
Built-in pluggable transformers for DeepSeek, OpenRouter, Gemini, MaxToken, etc., allowing multiple transformations to be stacked on a single model.
Supports both CLI and GitHub Actions modes; combined with Actions, you can leverage DeepSeek’s 25% nighttime rates for “early morning batch processing.”
The maxtoken transformer prevents CCR from accidentally exceeding SiliconFlow’s 16K max_tokens limit.
If you want to feed K2 all 32K+ project documentation in the same session, first use /model siliconflow,moonshotai/Kimi-K2-Instruct, then paste the full text.
CCR’s /model provider,model command supports hot-switching anytime; you can also preset default models for different task channels in Router, achieving a pipeline of “use R1 for coding, use K2 for long text reading.”
Slow response? DeepSeek’s official endpoint inference is relatively slow. If VS Code times out, enable UltraThink budget or switch to VolcEngine mirror.
Context overflow? DeepSeek API defaults to 64K, the above example uses VolcEngine to expand to 128K; Kimi K2 is 16K, needs maxtoken protection.
Wrong model name? CCR’s /model format must be provider,model, don’t forget the comma.
Tool calls failing? R1 currently doesn’t support tool_choice. For function calls, fall back to DeepSeek V3, or add a tooluse transformer to V3 in CCR.
With Claude Code Router + DeepSeek R1 + Kimi K2, you can hot-switch between “reasoning power” and “long context” strengths in the same IDE: use K2 for long document reports during the day, switch to R1 for batch code migration at night, at less than one-tenth the cost of official Claude Max. Install CCR now and unlock your new multi-model coding experience!
claude-trace is an essential debugging tool for Claude Code users, letting you see inside the AI’s “black box.” Whether developing plugins, analyzing model responses, or building Claude engineering log systems, it brings tremendous convenience.
If you’re developing projects with Claude, give it a try and start your “Claude visual development journey” with one click!
Coding Agents (like Claude Code, Gemini CLI, Amp, etc.) are becoming increasingly important in engineering development. Teams are finding that the time spent on planning, reviewing, and coordinating these human tasks now exceeds the time spent writing code itself.
Vibe Kanbanhttps://github.com/BloopAI/vibe-kanban is a new tool specifically designed to solve these collaboration workflow problems. Through its Kanban board interface, it allows you to:
Quickly switch between coding agents (Claude, Gemini, Amp, etc.);
Call multiple agents in parallel or series, automatically executing tasks;
Visualize task status, code reviews, and quickly launch Dev Servers;
Centrally manage agent MPC configurations to improve overall collaboration efficiency;
In the future, coding agents will be able to automatically create task tickets through MCP servers.
Vibe Kanban’s design philosophy is: When most code is written by AI, our human role becomes planning, reviewing, and coordinating. This is not just a trend, but a completely new way of development work.
Pre-configure Agents
For example, install and log into Claude Code, Gemini CLI, Amp, etc.
Run command to start Vibe Kanban
Terminal window
npxvibe-kanban
Automatically finds local port and opens browser (e.g., http://127.0.0.1:53046), entering the Kanban workspace.
Configure Agents
Initially, you need to add Agent MCP in Settings (e.g., Claude, Gemini, Amp tokens/paths), then you can freely switch agents in the UI to execute tasks.
Create Project
Create a new project in the Kanban interface, add Tasks, and you can start using it.
After switching to the corresponding agent, you can initiate multiple “Attempts” for the same task.
Supports parallel execution (different branches/workspaces) and serial execution (waiting for the previous step to complete before proceeding). Execution status and logs are clear at a glance.
After each agent generates code, Vibe Kanban automatically displays diffs and execution output, providing a one-click Dev Server launch button for real-time preview.
Uses the common three-column Kanban layout (To Do, In Progress, Done), providing an intuitive view of task status and agent execution progress.
You can create multiple attempts for a task and mark them as success/failure.
Vibe Kanban has a built-in “Vibe Kanban MCP” server plugin that can automatically generate planner tickets through agents. For example:
“Plan a migration from AWS to Azure and create detailed tickets…”
The agent will automatically break down tasks and create multiple Kanban cards, all done in the UI without manual intervention.
For individual developers:
Greatly improves task startup, review, and debugging efficiency, making development smoother and more structured.
For small team collaboration:
Agents automatically break down tasks, multiple attempts collaborate to complete, with independent Git worktrees, can connect to external MCP, suitable for team pipeline collaboration.
Investing in future capabilities:
As agent capabilities continue to improve, Vibe Kanban can seamlessly adapt to different agents, allowing you to experience the latest model capabilities at any time.
Vibe Kanban is pioneering the new paradigm of “Agent-driven, human-orchestrated”. From the era of writing code to the era of orchestrating and reviewing code, we will unleash human creativity and make Agent Handoff more efficient with clearer processes. We recommend all developers who are testing or using coding agents in production to give it a try.
ccusage is a command-line tool created by open-source developer ryoppippi for analyzing locally generated JSONL files from Claude Code. It displays your token usage and corresponding costs through visual tables, helping you better manage usage habits and cost structures.
Simple functionality: Get daily, monthly, session, and billing block (5-hour) level token usage and cost distribution with just one command.
Real-time monitoring: Supports blocks --live for real-time monitoring of consumption rates and project progress within the current billing block.
Lightweight and efficient: Extremely small size (npm package ~1 MB), no dependency installation required, suitable for quick analysis.
Cost awareness
While Claude Code’s Max version offers unlimited usage, when switching to pay-per-use plans, ccusage can help you estimate daily and monthly costs, considering your “pain-free threshold.”
Daily data tracking
Through daily or monthly reports, timely grasp long-term token usage trends and identify any sudden spikes or anomalies.
Session/billing block monitoring
Session level: Analyze single conversation token usage distribution and costs corresponding to projects and topics.
Block monitoring: Track consumption trends and rates within five-hour billing cycles (add --live for real-time observation).
Multi-terminal sync analysis
ccusage defaults to reading JSONL files from ~/.config/claude/projects/ and legacy path ~/.claude/projects/, enabling unified multi-device data analysis with cloud sync.
Secondary visualization and tool integration
Provides JSON output for integration with tools like Raycast, Neovim plugins, Go or Rust versions of real-time visualization tools (such as ccusage.nvim, ccusage-monitor, etc.).
Quickly see daily total tokens, cache hit counts, and estimated costs.
Using ccusage blocks --live, the console will refresh in real-time showing current billing block accumulated tokens, burn rate, remaining budget, etc., like a CLI version “htop”.
Cost estimates are simulated values: ccusage displays estimated costs based on token quantities and model pricing, which may slightly differ from actual bills.
ccusage is a compact but powerful CLI tool, suitable for developers and heavy users:
Quickly get daily/session/billing segment token consumption and cost analysis;
Real-time monitoring of current billing block progress;
Seamless integration with Raycast, Neovim, Go/Rust tools;
Cross-device analysis only requires syncing JSONL files.
Try npx ccusage daily and you’ll be surprised by your “spending trajectory” in Claude Code usage, becoming more conscious about budget management. Follow our channel to explore more efficiency secrets in the Claude ecosystem!
📌 Finally, if you want to learn more AI, Vibe Coding, and Claude usage tips, integration plugin recommendations, or advanced analysis, welcome to leave comments for discussion. Let’s build a smarter AI usage experience together!
